The aerodynamic force measurement technology during the acceleration phase of the motion model is one of the key technical challenges in the maglev flight wind tunnel. The aerodynamic force measurement balance is subjected to various strong interferences such as inertial forces in the acceleration section, which masks the measured aerodynamic force values. In order to restore the aerodynamic force signal under the harsh conditions of strong interference and low signal-to-noise ratio, this study proposed an aerodynamic force interference stripping algorithm based on deep learning. Firstly, the short-time Fourier transform was applied to the balance signal to determine the spectral characteristics of each interference, which was used as the input of the algorithm model. Then, an "encoder-decoder" architecture model was constructed to extract features from the complex signals measured by the balance and accurately reconstruct the desired aerodynamic force signal. After a comprehensive evaluation on the test set, the proposed algorithm demonstrated excellent performance in aerodynamic interference stripping, achieving a corresponding reconstruction accuracy of approximately 92.7% for the drag, lift, and pitching moment components. This study provides strong support for the aerodynamic force measurement in the future test environment of the maglev flight wind tunnel.
Marvin Tigre Larschow, Simon Thissen, Jakob Gugliuzza
et al.
With the increasing integration of low-temperature waste heat systems in aviation, large areas are needed for heat dissipation without causing significant pressure losses. Large-area skin heat exchangers (SHXs) are coming into focus as a possible solution. SHXs based on composite materials offer a promising approach due to their weight-saving potential. This article presents a structure-integrated SHX with a folded core using modern materials and design strategies. An analytical 1D heat transfer model, validated by measurements with temperature-sensitive paints (TSPs), was derived to efficiently identify the optimal parameter set in the design process of an SHX. The model focuses on transverse heat conduction effects in the facesheet for lateral heat distribution and uses these specifically for the overall mass-optimized configuration of the SHX. It is shown that with an optimally selected distance between the cooling channels in the case considered here, up to 12% more energy can be dissipated in relation to the total mass of the SHX. This article concludes with a sensitivity analysis of the analytical model. The influence of heat transfer, thermal conductivity in two spatial directions, and facesheet thickness on the optimal channel spacing is examined.
Rapid transit of emergency vehicles is critical for saving lives and reducing property loss but often relies on surrounding ordinary vehicles to cooperatively adjust their driving behaviors. It is important to ensure rapid transit of emergency vehicles while minimizing the impact on ordinary vehicles. Centralized mathematical solver and reinforcement learning are the state-of-the-art methods. The former obtains optimal solutions but is only practical for small-scale scenarios. The latter implicitly learns through extensive centralized training but the trained model exhibits limited scalability to different traffic conditions. Hence, existing methods suffer from two fundamental limitations: high computational cost and lack of scalability. To overcome above limitations, this work proposes a scalable distributed vehicle control method, where vehicles adjust their driving behaviors in a distributed manner online using only local instead of global information. We proved that the proposed distributed method using only local information is approximately equivalent to the one using global information, which enables vehicles to evaluate their candidate states and make approximately optimal decisions in real time without pre-training and with natural adaptability to varying traffic conditions. Then, a distributed conflict resolution mechanism is further proposed to guarantee vehicles' safety by avoiding their decision conflicts, which eliminates the single-point-of-failure risk of centralized methods and provides deterministic safety guarantees that learned methods cannot offer. Compared with existing methods, simulation experiments based on real-world traffic datasets demonstrate that the proposed method achieves faster decision-making, less impact on ordinary vehicles, and maintains much stronger scalability across different traffic densities and road configurations.
Soulaimane Idiri, Mohammed Said Boukhryss, Abdellah Azmani
et al.
This paper details the development of an embedded system for vehicle data acquisition using the On-Board Diagnostics version 2 (OBD2) protocol, with the objective of predicting power loss caused by exhaust gas backpressure (EBP). The system decodes and preprocesses vehicle data for subsequent analysis using predictive artificial intelligence algorithms. MATLAB’s 2023b Powertrain Blockset, along with the pre-built “Compression Ignition Dynamometer Reference Application (CIDynoRefApp)” model, was used to simulate engine behavior and its subsystems. This model facilitated the control of various engine subsystems and enabled simulation of dynamic environmental factors, including wind. Manipulation of the exhaust backpressure orifice revealed a consistent correlation between backpressure and power loss, consistent with theoretical expectations and prior research. For predictive analysis, two deep learning models—Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)—were applied to the generated sensor data. The models were evaluated based on their ability to predict engine states, focusing on prediction accuracy and performance. The results showed that GRU achieved lower Mean Absolute Error (MAE) and Mean Squared Error (MSE), making GRU the more effective model for power loss prediction in automotive applications. These findings highlight the potential of using synthetic data and deep learning techniques to improve predictive maintenance in the automotive industry.
Mechanical engineering and machinery, Machine design and drawing
We present a machine learning approach that leverages persistent homology to classify bacterial flagellar motors into two functional states: rotated and stalled. By embedding protein structural data into a topological framework, we extract multiscale features from filtered simplicial complexes constructed over atomic coordinates. These topological invariants, specifically persistence diagrams and barcodes, capture critical geometric and connectivity patterns that correlate with motor function. The extracted features are vectorized and integrated into a machine learning pipeline that includes dimensionality reduction and supervised classification. Applied to a curated dataset of experimentally characterized flagellar motors from diverse bacterial species, our model demonstrates high classification accuracy and robustness to structural variation. This approach highlights the power of topological data analysis in revealing functionally relevant patterns beyond the reach of traditional geometric descriptors, offering a novel computational tool for protein function prediction.
Patients with motor dysfunction show low subjective engagement in rehabilitation training. Traditional SSVEP-based brain-computer interface (BCI) systems rely heavily on external visual stimulus equipment, limiting their practicality in real-world settings. This study proposes an augmented reality steady-state visually evoked potential (AR-SSVEP) system to address the lack of patient initiative and the high workload on therapists. Firstly, we design four HoloLens 2-based EEG classes and collect EEG data from seven healthy subjects for analysis. Secondly, we build upon the conventional CNN-BiLSTM architecture by integrating a multi-head attention mechanism (MACNN-BiLSTM). We extract ten temporal-spectral EEG features and feed them into a CNN to learn high-level representations. Then, we use BiLSTM to model sequential dependencies and apply a multi-head attention mechanism to highlight motor-intention-related patterns. Finally, the SHAP (SHapley Additive exPlanations) method is applied to visualize EEG feature contributions to the neural network's decision-making process, enhancing the model's interpretability. These findings enhance real-time motor intention recognition and support recovery in patients with motor impairments.
The real-time assessment of complex motor skills presents a challenge in fields such as surgical training and rehabilitation. Recent advancements in neuroimaging, particularly functional near-infrared spectroscopy (fNIRS), have enabled objective assessment of such skills with high accuracy. However, these techniques are hindered by extensive preprocessing requirements to extract neural biomarkers. This study presents a novel end-to-end deep learning framework that processes raw fNIRS signals directly, eliminating the need for intermediate preprocessing steps. The model was evaluated on datasets from three distinct bimanual motor tasks--suturing, pattern cutting, and endotracheal intubation (ETI)--using performance metrics derived from both training and retention datasets. It achieved a mean classification accuracy of 93.9% (SD 4.4) and a generalization accuracy of 92.6% (SD 1.9) on unseen skill retention datasets, with a leave-one-subject-out cross-validation yielding an accuracy of 94.1% (SD 3.6). Contralateral prefrontal cortex activations exhibited task-specific discriminative power, while motor cortex activations consistently contributed to accurate classification. The model also demonstrated resilience to neurovascular coupling saturation caused by extended task sessions, maintaining robust performance across trials. Comparative analysis confirms that the end-to-end model performs on par with or surpasses baseline models optimized for fully processed fNIRS data, with statistically similar (p<0.05) or improved prediction accuracies. By eliminating the need for extensive signal preprocessing, this work provides a foundation for real-time, non-invasive assessment of bimanual motor skills in medical training environments, with potential applications in robotics, rehabilitation, and sports.
The Cislunar economy is thriving with innovative space systems and operation techniques to enhance and uplift the traditional approaches significantly. This paper brings about an approach for sustainable small satellite constellations to retain autonomy for long-term missions in the Cislunar space. The methodology presented is to align the hybrid model of the constellation for Earth and Moon as an integral portion of the Cislunar operations. These hybrid constellations can provide a breakthrough in optimally utilizing the Cislunar space to efficiently deploy prominent missions to be operated and avoid conjunction or collisions forming additional debris. Flower and walker constellation patterns have been combined to form a well-defined orientation for these small satellites to operate and deliver the tasks satisfying the mission objectives. The autonomous multi-parametric analysis for each constellation based in Earth and Moon’s environment has been attained with due consideration to local environments. Specifically, the Solar Radiation Pressure (SRP) is a critical constraint in Cislunar operations and is observed during simulations. These are supported by conjunction analysis using the Monte Carlo technique and also the effect of the SRP on the operating small satellites in real-time scenarios. This is followed by the observed conclusions and the way forward in this fiercely competent Cislunar operation.
Abdullah Alawadhi, Constantine Eliopoulos, Frederic Bezombes
For the first time, RGB and multispectral sensors deployed on UAVs were used to facilitate grave detection in a desert location. The research sought to monitor surface anomalies caused by burials using manual and enhanced detection methods, which was possible up to 18 months. Near-IR (NIR) and Red-Edge bands were the most suitable for manual detection, with a 69% and 31% success rate, respectively. Meanwhile, the enhanced method results varied depending on the sensor. The standard Reed–Xiaoli Detector (RXD) algorithm and Uniform Target Detector (UTD) algorithm were the most suitable for RGB data, with 56% and 43% detection rates, respectively. For the multispectral data, the percentages varied between the algorithms with a hybrid of the RXD and UTD algorithms yielding a 56% detection rate, the UTD algorithm 31%, and the RXD algorithm 13%. Moreover, the research explored identifying grave mounds using the normalized digital surface model (nDSM) and evaluated using the normalized difference vegetation index (NDVI) in grave detection. nDSM successfully located grave mounds at heights as low as 1 cm. A noticeable difference in NDVI values was observed between the graves and their surroundings, regardless of the extreme weather conditions. The results support the potential of using RGB and multispectral sensors mounted on UAVs for detecting burial sites in an arid environment.
This paper investigates the distributed formation control of a group of leader-following spacecraft with bounded actuation and limited communication ranges. In particular, connectivity-preserving and collision-avoidance controllers are proposed for the leader with constant or time-varying velocity, respectively. The communication graph between the spacecraft is modeled via a distance-induced proximity graph. By designing a virtual proxy for each spacecraft, the spacecraft–proxy couplings address the actuator saturation constraints. The inter-proxy dynamics incorporated with a bounded artificial potential function fulfill the coordination of all proxies. In addition, the bounded potential function can simultaneously tackle connectivity preservation and collision avoidance problems. The distributed formation controllers are proposed for multiple spacecraft with constant or time-varying velocities relative to the leader. A sliding mode control approach and the proxies’ dynamics are used in the design of a distributed cooperative controller for spacecraft to address the cooperative problem between the followers and the leader. Numerical simulations confirm the effectiveness of the anti-saturation distributed connectivity preservation controller.
Fang Sheng, Mao Kai, Wang Manxi, Hua Boyu, Song Maozhong, Zhu Qiuming
Considering the rapid time-varying channel conditions and large Doppler frequency fluctuations caused by high-speed movement of unmanned aerial vehicles, this paper proposes a non-stationary channel emulation scheme for Unmanned Aerial Vehicles(UAVs) using a field-programmable gate array platform. The proposed scheme adopts the sum of frequency modulation method to generate non-stationary channel fading and proposes a real-time algorithm for generating channel parameters to improve the real-time performance of channel emulation and ensure the real-time updating of channel status. In addition, to address the issue of power random fluctuation caused by large Doppler frequency fluctuations, this paper designs an adaptive power equalization module that ensures the stability of fading power by limiting the maximum fluctuation to only 1.13%. Finally, the results of hardware resource consumption analysis demonstrate that the proposed approach in this study significantly reduces the utilization of storage resources compared to replay-based and pre-stored solutions, with reductions of 52.44% and 9.31%, respectively. This makes the proposed approach well-suited for simulating long-duration non-stationary channel fading in UAV scenarios. Additionally, the measured analysis results demon-strate that the channel characteristics output by the proposed hardware emulation scheme, such as path loss and Doppler power spectrum density,closely align with theoretical results when compared to the emulation scheme without the equalizer. This research can be applied to the design and optimization of UAVs communication systems.
Soichi Hirokawa, Heun Jin Lee, Rachel A Banks
et al.
Motor-driven cytoskeletal remodeling in cellular systems can often be accompanied by a diffusive-like effect at local scales, but distinguishing the contributions of the ordering process, such as active contraction of a network, from this active diffusion is difficult to achieve. Using light-dimerizable kinesin motors to spatially control the formation and contraction of a microtubule network, we deliberately photobleach a grid pattern onto the filament network serving as a transient and dynamic coordinate system to observe the deformation and translation of the remaining fluorescent squares of microtubules. We find that the network contracts at a rate set by motor speed but is accompanied by a diffusive-like spread throughout the bulk of the contracting network with effective diffusion constant two orders of magnitude lower than that for a freely-diffusing microtubule. We further find that on micron scales, the diffusive timescale is only a factor of approximately 3 slower than that of advection regardless of conditions, showing that the global contraction and long-time relaxation from this diffusive behavior are both motor-driven but exhibit local competition within the network bulk.
Bio-engineered robots are under rapid development due to their maneuver ability through uneven surfaces. This advancement paves the way for experimenting with versatile electrical system developments with various motors. In this research paper, we present a design, fabrication and analysis of a versatile printed circuit board (PCB) as the main system that allows for the control of twelve stepper motors by stacking low-budget stepper motor controller and widely used micro-controller unit. The primary motivation behind the design is to offer a compact and efficient hardware solution for controlling multiple stepper motors of a quadruped robot while meeting the required power budget. The research focuses on the hardware's architecture, stackable design, power budget planning and a thorough analysis. Additionally, PDN (Power Distribution Network) analysis simulation is done to ensure that the voltage and current density are within the expected parameters. Also, the hardware design deep dives into design for manufacturability (DFM). The ability to stack the controllers on the development board provides insights into the board's components swapping feasibility. The findings from this research make a significant contribution to the advancement of stepper motor control systems of multi-axis applications for bio-inspired robot offering a convenient form factor and a reliable performance.
With the development of aerospace technology, protective materials for hot-end components have reached higher requirements. In this paper, a （ZrxY（1-x/4）Ta（1-x/4）Ti（1-x/4）Er（1-x/4））O（x=0.2，0.544，0.672，0.796和0.92）quintuple element ceramic system composite is studied based on the solid-phase reaction method and molecular dynamics simulation. By experimental means, ZrO2 （99.99%）, Y2O3 （99.99%）, Ta2O5 （99.99%）, Er2O3 （99.99%） and TiO2 （99%） powder was used as raw material to prepare （ZrxY（1-x/4）Ta（1-x/4）Ti（1-x/4）Er（1-x/4））O composite by the solid-phase reaction method. The thermal conductivity of （ZrxY（1-x/4）Ta（1-x/4）Ti（1-x/4）Er（1-x/4））O ceramic material was investigated computationally using the LAMMPS program. The study result shows that a consistent trend in the variation of the thermal conductivity is obtained by experiments and simulations at the interval of 200-900 °C. The thermal conductivity reaches a minimum value at x = 0.796, which proves the feasibility of molecular dynamics simulation of the thermal conductivity of multi-ceramic materials. Meanwhile, the effect of porosity on thermal conductivity was investigated, and it is found that there was a competitive relationship between the elemental ratios and the effect of porosity on thermal conductivity. When the porosity is larger than 6.67%, the effect of the porosity is the main influencing factor. when the porosity is smaller than 6.67%, the elemental ratios are the dominant factors in the thermal conductivity.
The performance software of a domestic civil aircraft used by an airline for many years can obtain satisfactory results in the accuracy of obstacle limited weight calculation, but the calculation time is long. The optimization model is established by comparing the calculation results and efficiency of the two models of the minimum acceleration height method and the maximum acceleration height method. The obstacle limited weight, acceleration height and calculation time of the two models under different obstacles and different wind speeds are calculated and analyzed. The results show that the calculation results of obstacle limited weight and acceleration height of the two models are basically the same; When there is no obstacle, whether there is wind or not, the calculation time can be reduced by 25% using the optimization model with the minimum acceleration height compared with the original model with the maximum acceleration height; When there are obstacles and there is no wind, the calculation time can be reduced by more than 78% by using the optimization model with minimum acceleration height,and when there is wind, the calculation time can be reduced by more than 75%. The model with the minimum acceleration height can take account of both accuracy and efficiency.
Prashant Deshmukh, Subhash Lahane, Hari Sumant
et al.
Heat transfer enhancement using curved ribs of different cross sections, viz., square, rectangular, triangular, and circular, is a crucial study for designing heat-exchanging devices for various applications, and their thermohydraulic performance prediction using machine learning technique is a vital part of the modern world. An experimental study on using curved ribs suitable for heat transfer enhancement for the circular tube is presented for turbulent airflow with Reynolds numbers varying from 10,000 to 50,000. The machine learning methodology is used to predict the thermohydraulic performance assessment of curved ribs. The square cross-sectioned curved ribs produce the highest performance factor <i>R3</i> of 1.5 to 2.65 to the equivalent Reynolds number <i>Rec</i> value of 20,000. It is observed that most of the curved rib configurations show a performance ratio <i>R3</i> maximum and are suitable at a low Reynolds number value. At moderate and high Reynolds number values, the performance factor values decrease due to a rise in the pressure drop values for a few curved rib configurations. An artificial neural network (ANN) model predicts with an accuracy of 95% with the present study experimental values for the heat transfer performance indicators like average heat transfer enhancement <i>Nua/Nus</i>, average heat transfer enhancement <i>fa/fs</i>, and performance ratio <i>R3</i>, i.e., <i>Nua/Nuc</i>.
The employment of unmanned aerial vehicles (UAVs) has greatly facilitated the lives of humans. Due to the mass manufacturing of consumer unmanned aerial vehicles and the support of related scientific research, it can now be used in lighting shows, jungle search-and-rescues, topographical mapping, disaster monitoring, and sports event broadcasting, among many other disciplines. Some applications have stricter requirements for the autonomous positioning capability of UAV clusters, requiring its positioning precision to be within the cognitive range of a human or machine. Global Navigation Satellite System (GNSS) is currently the only method that can be applied directly and consistently to UAV positioning. Even with dependable GNSS, large-scale clustering of drones might fail, resulting in drone cluster bombardment. As a type of passive sensor, the visual sensor has a compact size, a low cost, a wealth of information, strong positional autonomy and reliability, and high positioning accuracy. This automated navigation technology is ideal for drone swarms. The application of vision sensors in the collaborative task of multiple UAVs can effectively avoid navigation interruption or precision deficiency caused by factors such as field-of-view obstruction or flight height limitation of a single UAV sensor and achieve large-area group positioning and navigation in complex environments. This paper examines collaborative visual positioning among multiple UAVs (UAV autonomous positioning and navigation, distributed collaborative measurement fusion under cluster dynamic topology, and group navigation based on active behavior control and distributed fusion of multi-source dynamic sensing information). Current research constraints are compared and appraised, and the most pressing issues to be addressed in the future are anticipated and researched. Through analysis and discussion, it has been concluded that the integrated employment of the aforementioned methodologies aids in enhancing the cooperative positioning and navigation capabilities of multiple UAVs during GNSS denial.
Model Predictive Control (MPC) has been widely applied to the motion planning of autonomous vehicles. An MPC-controlled vehicle is required to predict its own trajectories in a finite prediction horizon according to its model. Beyond this, the vehicle should also incorporate the prediction of the trajectory of its nearby vehicles, or target vehicles (TVs) into its decision-making. The conventional trajectory prediction methods, such as the constant-speed-based ones, are too trivial to accurately capture the potential collision risks. In this report, we propose a novel MPC-based motion planning method for an autonomous vehicle with a set of risk-aware constraints. These constraints incorporate the predicted trajectory of a TV learned using a deep-learning-based method. A recurrent neural network (RNN) is used to predict the TV's future trajectory based on its historical data. Then, the predicted TV trajectory is incorporated into the optimization of the MPC of the ego vehicle to generate collision-free motion. Simulation studies are conducted to showcase the prediction accuracy of the RNN model and the collision-free trajectories generated by the MPC.
During the flight mission of hypersonic aircraft, severe aerodynamic heating will occur on the surface, so thermal protection system (TPS) is required to protect the load-bearing structure of the aircraft. The present paper develops an engineering software for automatic optimization of the thickness of tile-type TPS for reusable aircraft. For requirements on TPS of reusable aircraft in the reentry stage, the method of heat flow-time curve enveloping, automatic material selection, and one-dimensional unsteady heat transfer calculation for multilayer plates under thermal load conditions had been researched, an interactive engineering software had been developed. The software improves the calculation accuracy and calculation efficiency of TPS thickness optimization, and it is suitable for rapid design in the conceptual design stage of the aircraft. Finally, by an example, the function of the software is verified.