Hasil untuk "Motor vehicles. Aeronautics. Astronautics"

Teerapong Panboonyuen

This handbook presents a systematic treatment of the foundations and architectures of artificial intelligence for motor insurance, grounded in large-scale real-world deployment. It formalizes a vertically integrated AI paradigm that unifies perception, multimodal reasoning, and production infrastructure into a cohesive intelligence stack for automotive risk assessment and claims processing. At its core, the handbook develops domain-adapted transformer architectures for structured visual understanding, relational vehicle representation learning, and multimodal document intelligence, enabling end-to-end automation of vehicle damage analysis, claims evaluation, and underwriting workflows. These components are composed into a scalable pipeline operating under practical constraints observed in nationwide motor insurance systems in Thailand. Beyond model design, the handbook emphasizes the co-evolution of learning algorithms and MLOps practices, establishing a principled framework for translating modern artificial intelligence into reliable, production-grade systems in high-stakes industrial environments.

Mahdi Hatam, Majid Hatam

In this paper, an improved pulse repetition frequency selection method for the spaceborne synthetic aperture radar is proposed, where the design considerations of the other system parameters including antenna dimensions, azimuth and range resolution, pulse width, swath width, incidence angle / look angle, orbital altitude and local radius of the earth are taken into account. In the proposed method, in addition to the ability to change the incidence angle, the effects of changes in the orbital altitude and the local radius of the earth are also considered in the design. Also, in this method, in order to stabilize the transmitted power, the design for constant duty cycle mode can be done accurately, which can be very important in a practical system. Also, by using fixed duty cycle in the proposed method, the range of available pulse repetition frequency values and incidence/look angles will significantly be increased. Finally, for a typical system, the results of computer simulation and design using the proposed method for the both constant duty cycle and constant pulse width modes are given, which confirms the above capabilities.

QIAN Guohong, WEN Zixia, QIU Xin et al.

After separating from the catapult and leaving the flight deck，carrier-based aircraft face multiple constraints including sink rate，angle of attack，and pitch rate to ensure flight safety and quality. Traditional angle-of-attack protection control methods tend to be overly conservative when the attack angle approaches to the stall attack angle，the control system automatically lowers the aircraft's attitude to reduce the angle of attack. While effective in preventing stall，this approach sacrifices partial lift，which is crucial for maintaining sufficient lift during the critical launch phase. To address these issues，a model predictive controller is designed that converts key performance metrics during catapult-assisted takeoff into linear matrix inequality（LMI）constraints. Through online receding horizon optimization，predictive feedback control laws are solved to guarantee asymptotic stability and robustness of the closed-loop system，enabling the aircraft to maintain maximum allowable angle of attack and sustain high-lift flight during the launch phase. The nonlinear simulation cases are validated. The results show that the proposed control method based in LMI constraints and model prediction can realize the sustained high lift flight of carrier based aircraft under multiple constraints during the catapult takeoff phase.

Surabhi Jaiswal, Prithwiraj Maity, Snigdha Thakur et al.

Polar polymer activity is a fundamental mechanism behind a large number of cellular dynamical processes. The number and location of the active sites on the polymer backbone play a central role in their dynamics and conformational properties. Globular conformations for high motor densities change to stretched ones for the more realistic moderate or low density of motors, with a self-propelled polymer velocity non-monotonically related to the motor density. A small difference in the position of the first motor, or the motor distribution, can also dramatically modify the polymer typical conformations

XING Yifeng, YIN Aobo, GENG Lilun et al.

As the turbine inlet temperatures of aero engines continue to rise， there is an urgent need to develop a new generation of single-crystal superalloys and their thermal protective coatings for turbine blades. In order to meet the stringent requirements for the comprehensive performance of high-temperature structural materials in the complex service environments of aero engines， the intelligent design research of single crystal superalloys and thermal protection coatings has been gradually carried out at home and abroad in recent years under the promotion of material integrated computational engineering and material informatics. This paper reviews the latest research progress in the design of novel single-crystal superalloys and thermal protective coatings by utilizing multi-scale computational simulations and machine learning methods. The findings confirm that multi-scale computational simulations offer robust theoretical support for understanding the strengthening and toughening mechanisms of single-crystal superalloys， as well as the oxidation resistance and diffusion protective mechanisms of thermal protective coatings. Additionally， the study highlights the reliability and significant potential of machine learning in constructing intrinsic "composition-structure-property" relationship for high-temperature structural materials. This approach paves an intelligent and efficient new pathway for the rapid development of next-generation high-temperature single-crystal superalloys and thermal protective coatings.

Sylvain Grosse, Eric Moreau, Nicolas Binder

The resurgence in interest in aircraft electro-aerodynamic (EAD) propulsion has been sparked due to recent advancements in EAD thrusters, which generate thrust by employing a plasma generated through electrical discharge. With potentially quieter propulsion that could contribute to the generation of lift or the control of attitude, it is important to determine the feasibility of an EAD-propelled airplane. First, the main propulsive characteristics (thrust generation and power consumption) of EAD thrusters were drawn from the literature and compared with existing technologies. Second, an algorithm was developed to couple standard equations of flight with EAD propulsion performance and treat the first-order interactions. It fairly replicated the performance of the only available autonomous EAD-propelled drone. A test case based on an existing commercial UAV of 10 kg equipped with current-generation EAD thrusters anticipated a flight of less than 10 min, lower than 30 m in height, and below 8 m · s ⁻¹ in velocity. Achieving over 2 h of flight at 30 m of height at 10 m · s ⁻¹ requires the current EAD thrust to be doubled without altering the power consumption. For the same flight performance as the baseline UAV, the prediction asked for a tenfold increase in the thrust at the same power consumption.

Wen Gao, Yanqiang Bi, Xiyuan Li et al.

Hybrid airships, combining aerodynamic lift and buoyant lift, are efficient near-space aircraft for scientific exploration, observation, and surveillance. Compared to conventional airplanes and airships, hybrid airships offer unique advantages, including stationary hovering and rapid deployment. Due to the different task requirements and strong coupling between flight and environment, trajectory-optimization methods for traditional aircraft are difficult to apply to hybrid airships directly. We propose a trajectory-optimization model based on the variational method to calculate the optimal time and energy paths under weak, uniform, and latitudinal linear wind fields. Our model shows that the influencing factors for the optimization path can be categorized into three types: airship design parameters, wind field parameters, and departure parameters. The result indicates that the optimal time paths are generally straight lines, and the optimal energy paths are piecewise curves with a 24-h cycle under typical hybrid airship design parameters. This work has provided new insight into the trajectory optimization and parameter design of future hybrid airships.

T. H. New, S. Mandrà

Abstract Steady-state numerical simulations were conducted to capture the aerodynamic characteristics and flow patterns resulting from a tubercled and non-tubercled wing subjected to various combined pitch and yaw conditions at $$Re=1.8 \times 10^{5}$$ R e = 1.8 × 10 5 . Pitch angle ranged from $$0^{\circ }$$ 0 ∘ to $$25^{\circ }$$ 25 ∘ , while two different yaw angles of $$10^{\circ }$$ 10 ∘ and $$30^{\circ }$$ 30 ∘ were used. Results show that $$10^{\circ }$$ 10 ∘ yaw angle does not impact upon the lift and drag characteristics significantly, while a $$30^{\circ }$$ 30 ∘ yaw angle leads to substantial lift and drag losses. Additionally, the tubercled wing continues to confer favourable stall-mitigating characteristics even for the larger yaw angle. Finally, despite skewing the flow structures significantly, the $$30^{\circ }$$ 30 ∘ yaw angle also reduces the formations of bi-periodic flow structures, flow separations and recirculating regions along the leading-edge tubercles, suggesting potentially better flow stability and controllability.

Meng Ma, Zhirong Zhong, Zhi Zhai et al.

There are hundreds of various sensors used for online Prognosis and Health Management (PHM) of LREs. Inspired by the fact that a limited number of key sensors are selected for inflight control purposes in LRE, it is practical to optimal placement of redundant sensors for improving the diagnosability and economics of PHM systems. To strike a balance between sensor cost, real-time performance and diagnosability of the fault diagnosis algorithm in LRE, this paper proposes a novel Optimal Sensor Placement (OSP) method. Firstly, a Kernel Extreme Learning Machine-based (KELM) two-stage diagnosis algorithm is developed based on a system-level failure simulation model of LRE. Secondly, hierarchical diagnosability metrics are constructed to formulate the OSP problem in this paper. Thirdly, a Hierarchy Ranking Evolutionary Algorithm-based (HREA) two-stage OSP method is developed, achieving further optimization of Pareto solutions by the improved hypervolume indicator. Finally, the proposed method is validated using failure simulation datasets and hot-fire test-run experiment datasets. Additionally, four classical binary multi-objective optimization algorithms are introduced for comparison. The testing results demonstrate that the HREA-based OSP method outperforms other classical methods in effectively balancing the sensor cost, real-time performance and diagnosability of the diagnosis algorithm. The proposed method in this paper implements system-level OSP for LRE fault diagnosis and exhibits the potential for application in the development of reusable LREs.

Zahra Amirsardari, Babak Afzali, Mohammad Reza Amirsoleimani

To discuss the potential effect of iridium (Ir) nanoparticles as an active loaded in atmospheric conditions, we prepared a series of catalysts with the same active phase, but different contents of iridium (10, 15, and 20% by weight) on gamma-alumina for decomposition of hydrazine. The performance of catalyst nanoparticles was better with 15wt% and 20wt% of the Ir particles, and also the selectivity to hydrogen was about 27%. An increase in the reaction rate from 181 h-1 to 218 h−1 was observed in loading 15% by weight of iridium particles due to the good dispersion of the active phases by preventing surface agglomeration. Therefore, as a satisfactory result of this investigation, it was found that Ir catalysts with different weight percentage (15wt% and 20wt%) show the same performance against the activity and selectivity to hydrogen, and are suitable substitutes for each other. Using a catalyst with a lower weight percentage of the active phase and high activity is economically acceptable due to its low cost.

Gangcheng Zhang, Yeshuo Shu, Keyi Liu et al.

The widespread use of e-bikes has facilitated short-distance travel yet led to confusion and safety problems in road traffic. This study focuses on the dual characteristics of e-bikes in traffic conflicts: they resemble pedestrians when interacting with motor vehicles and behave like motor vehicles when in conflict with pedestrians, which raises the right of way concerns when potential conflicts are at stake. Using the Quantal Response Equilibrium model, this research analyzes the behavioral choice differences of three groups of road users (vehicle-pedestrian, vehicle-e-bike, e-bike-pedestrian) at right-turn-on-red crossroads in right-turning lines and straight-going lines conflict scenarios. The results show that the behavior of e-bikes is more similar to that of motor vehicles than pedestrians overall, and their interactions with either pedestrians or motor vehicles do not establish a reasonable order, increasing the likelihood of confusion and conflict. In contrast, a mutual understanding has developed between motor vehicles and pedestrians, where motor vehicles tend to yield, and pedestrians tend to cross. By clarifying the game theoretical model and introducing the rationality parameter, this study precisely locates the role of e-bikes among road users, which provides a reliable theoretical basis for optimizing traffic regulations.

Olaf Borsboom, Arnab Bhadra, Mauro Salazar et al.

In this paper, we present geometric scaling models for axial flux motors (AFMs) to be used for in-wheel powertrain design optimization purposes. We first present a vehicle and powertrain model, with emphasis on the electric motor model. We construct the latter by formulating the analytical scaling laws for AFMs, based on the scaling concept of RFMs from the literature, specifically deriving the model of the main loss component in electric motors: the copper losses. We further present separate scaling models of motor parameters, losses and thermal models, as well as the torque limits and cost, as a function of the design variables. Second, we validate these scaling laws with several experiments leveraging high-fidelity finite-element simulations. Finally, we define an optimization problem that minimizes the energy consumption over a drive cycle, optimizing the motor size and transmission ratio for a wide range of electric vehicle powertrain topologies. In our study, we observe that the all-wheel drive topology equipped with in-wheel AFMs is the most efficient, but also generates the highest material cost.

Usman Ali

A method for determining the current signature faults using Fractional Fourier Transform (FrFT) has been developed. The method has been applied to the real-time steady-state current of the inverter-fed high power induction motor for fault determination. The method incorporates calculating the relative norm error to find the threshold value between healthy and unhealthy induction motor at different operating frequencies. The experimental results demonstrate that the total harmonics distortion of unhealthy motor is much larger than the healthy motor, and the threshold relative norm error value of different healthy induction motors is less than 0.3, and the threshold relative norm error value of unhealthy induction motor is greater than 0.5. The developed method can function as a simple operator-assisted tool for determining induction motor faults in real-time.

Lin Zhang, Mingyang Wang, Yunhong Ding et al.

Forest fires are one of the most serious natural disasters that threaten forest resources. The early and accurate identification of forest fires is crucial for reducing losses. Compared with satellites and sensors, unmanned aerial vehicles (UAVs) are widely used in forest fire monitoring tasks due to their flexibility and wide coverage. The key to fire monitoring is to accurately segment the area where the fire is located in the image. However, for early forest fire monitoring, fires captured remotely by UAVs have the characteristics of a small area, irregular contour, and susceptibility to forest cover, making the accurate segmentation of fire areas from images a challenge. This article proposes an FBC-ANet network architecture that integrates boundary enhancement modules and context-aware modules into a lightweight encoder–decoder network. FBC-Anet can extract deep semantic features from images and enhance shallow edge features, thereby achieving an effective segmentation of forest fire areas in the image. The FBC-ANet model uses an Xception network as the backbone of an encoder to extract features of different scales from images. By transforming the extracted deep semantic features through the CIA module, the model’s feature learning ability for fire pixels is enhanced, making feature extraction more robust. FBC-ANet integrates the decoder into the BEM module to enhance the extraction of shallow edge features in images. The experimental results indicate that the FBC-ANet model has a better segmentation performance for small target forest fires compared to the baseline model. The segmentation accuracy on the dataset FLAME is 92.19%, the F1 score is 90.76%, and the IoU reaches 83.08%. This indicates that the FBC-ANet model can indeed extract more valuable features related to fire in the image, thereby better segmenting the fire area from the image.

Arion Pons

Tiny flying insects, such as Drosophila melanogaster, fly by flapping their wings at frequencies faster than their brains are able to process. To do so, they rely on self-oscillation: dynamic instability, leading to emergent oscillation, arising from muscle stretch-activation. Many questions concerning this vital natural instability remain open. Does flight motor self-oscillation necessarily lead to resonance - a state optimal in efficiency and/or performance? If so, what state? And is self-oscillation even guaranteed in a motor driven by stretch-activated muscle, or are there limiting conditions? In this work, we use data-driven models of wingbeat and muscle behaviour to answer these questions. Developing and leveraging novel analysis techniques, including symbolic computation, we establish a fundamental condition for motor self-oscillation common to a wide range of motor models. Remarkably, D. melanogaster flight apparently defies this condition: a paradox of motor operation. We explore potential resolutions to this paradox, and, within its confines, establish that the D. melanogaster flight motor is likely not resonant with respect to exoskeletal elasticity: instead, the muscular elasticity plays a dominant role. Contrary to common supposition, the stiffness of stretch-activated muscle is an obstacle to, rather than an enabler of, the operation of the D. melanogaster flight motor.

YANG Shu, WANG Yihua

In recent years,with the rapid development of UAV transportation industry,the problem of conflict detection and collision avoidance during its flight has become a key problem to be solved.A reasonable three-dimensional spatial model is established around the UAV,a three-level collision avoidance area system including emergency collision avoidance area,general collision avoidance area,surveillance and advance collision avoidance area is optimized,and the information such as UAV position and speed provided by ADS-B(automatic dependent surveillance-broadcast)messages is used,based on the general two-dimensional plane of UAV conflict detection and collision avoidance algorithm,and the conflict detection algorithm is improved by adding conflict recognition in the vertical direction,and the success rates of the two avoidance schemes,speed and direction,are compared in each collision avoidance region.The results show that the algorithm can effectively identify conflicting UAVs when the number of UAVs increases significantly,while the success rate of collision avoidance using the speed-then-direction avoidance strategy reaches 99.75%,providing an effective strategy for ensuring the flight safety of UAVs.

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Karthik Urs, Challen Enninful Adu, Elliott J. Rouse et al.

Robotic systems for legged locomotion -- including legged robots, exoskeletons, and prosthetics -- require actuators with low inertia and high output torque. Traditionally, motors have been selected for these applications by maximizing the motor gap radius. We present alternative metrics for motor selection that are invariant to transmission ratio. The proposed metrics reward minimizing the motor inertia while maximizing the torque and motor constants without special consideration for gap radius, providing a better balance of properties for legged locomotion applications. We rigorously characterize the T-Motor RI50 and demonstrate the use of the metrics by comparing the RI50 to the widely-used T-Motor U8 as a case study.

Maria-Veronica Ciocanel, Aravind Chandrasekaran, Carli Mager et al.

Cortical actin networks are highly dynamic and play critical roles in shaping the mechanical properties of cells. The actin cytoskeleton undergoes significant reorganization over the course of the cell cycle, when cortical actin transitions between open patched meshworks, homogeneous distributions, and aligned bundles. Several types of myosin motor proteins, characterized by different kinetic parameters, have been involved in this reorganization of actin filaments. Given the limitations in studying the interactions of actin with myosin in vivo, we propose stochastic agent-based model simulations and develop a set of data analysis measures to assess how myosin motor proteins mediate various actin organizations. In particular, we identify individual motor parameters, such as motor binding rate and step size, that generate actin networks with different levels of contractility and different patterns of myosin motor localization. In simulations where two motor populations with distinct kinetic parameters interact with the same actin network, we find that motors may act in a complementary way, by tuning the actin network organization, or in an antagonistic way, where one motor emerges as dominant. This modeling and data analysis framework also uncovers parameter regimes where spatial segregation between motor populations is achieved. By allowing for changes in kinetic rates during the actin-myosin dynamic simulations, our work suggests that certain actin-myosin organizations may require additional regulation beyond mediation by motor proteins in order to reconfigure the cytoskeleton network on experimentally-observed timescales.

Rayyan Bin Fairuz

Condition monitoring of induction motor has been widely researched over recent years due to its ability to monitor operating characteristics and the health status of induction motor. Various methods have been used to monitor induction motors such as thermal monitoring and vibration analysis. This paper introduces an alternative method which is to use an inductive coupling method to extract in-circuit impedance of induction motor. This method allows for an online measurement of the system under test (SUT) preventing any unnecessary shutdowns. These unnecessary shutdowns may incur a loss of revenue for relevant industries that depends heavily on induction motor operations. Two sets of experiments were conducted in this paper, the first experiment was to examine the accuracy of the proposed method by simulating various SUT using simulated resistor, inductor, and capacitor (RLC) network. The next experiment was to detect incipient stator faults such as turn to turn faults in an induction motor. This proposed method measures the impedance of the stator winding of an induction motor to identify abnormalities. In addition, this method allows for an accurate in-circuit impedance measurement without the influence of motor load and frequency changes as well as faults such as bearing and rotor faults.