Time-of-flight Research Articles

Transition flight of a concept of lifting-wing quadcopter

Abstract This paper presents the concept of a lifting-wing quadcopter unmanned aerial vehicle (UAV), a vertical take-off and landing vehicle (VTOL) with a rear wing, a canard at its front and four propellers. The aerodynamic surfaces are designed so that their mounting angle can be adjusted and fixed before flight, so its performance in transition flight can be studied for a combination of wing and canard mounting angles. A dynamic model using rigid-body equations of motion is presented, which is used to compute the transition flight trajectory from hover to cruise in horizontal flight. The trim conditions were computed for a range of fixed wing and canard mounting angles to study the effects of these variables on transition trajectory parameters such as required power, body pitch angle and propeller rotation speeds as a function of flight speed. Furthermore, a transition flight control algorithm is presented, which has a cascaded PID controller and a reference scheduler to switch between the proper reference states, controls and control allocation matrix. Finally, the transition control algorithm of the conceptual UAV is numerically simulated. Results show that this configuration can perform a fast and smooth transition from hover to cruise flight using the proposed flight control algorithm, substantially reducing required propulsive power in cruise of up to 64%. The application of the control algorithm made notable a transition manoeuver that consists of negatively inclining the aircraft at a negative pitch angle, initially at high intensity, and as the final cruising speed approaches, the inclination is attenuated until the equilibrium pitch angle is reached. Simultaneously with the negative inclination of the pitch angle, there is a slight drop in altitude, which is quickly resumed as the trajectory develops until the final cruising speed. Lastly, this aircraft configuration can be widely used in applications where performance gains in operations currently carried out by multicopters, which cover large distances and need long flight time, would bring great operational advantages.

An Energy-Efficient Dynamic Feedback Image Signal Processor for Three-Dimensional Time-of-Flight Sensors

With the recent prominence of artificial intelligence (AI) technology, various research outcomes and applications in the field of image recognition and processing utilizing AI have been continuously emerging. In particular, the domain of object recognition using 3D time-of-flight (ToF) sensors has been actively researched, often in conjunction with augmented reality (AR) and virtual reality (VR). However, for more precise analysis, high-quality images are required, necessitating significantly larger parameters and computations. These requirements can pose challenges, especially in developing AR and VR technologies for low-power portable devices. Therefore, we propose a dynamic feedback configuration image signal processor (ISP) for 3D ToF sensors. The ISP achieves both accuracy and energy efficiency through dynamic feedback. The proposed ISP employs dynamic area extraction to perform computations and post-processing only for pixels within the valid area used by the application in each frame. Additionally, it uses dynamic resolution to determine and apply the appropriate resolution for each frame. This approach enhances energy efficiency by avoiding the processing of all sensor data while maintaining or surpassing accuracy levels. Furthermore, These functionalities are designed for hardware-efficient implementation, improving processing speed and minimizing power consumption. The results show a maximum performance of 178 fps and a high energy efficiency of up to 123.15 fps/W. When connected to the hand pose estimation (HPE) accelerator, it demonstrates an average mean squared error (MSE) of 10.03 mm, surpassing the baseline ISP value of 20.25 mm. Therefore, the proposed ISP can be effectively utilized in low-power, small form-factor devices.

Leveraging the Interplanetary Superhighway for Propellant–Optimal Orbit Insertion into Saturn–Titan System

This paper presents an innovative approach using Dynamical Systems Theory (DST) for interplanetary orbit insertion into Saturn−Titan three−body orbits. By leveraging DST, this study identifies invariant manifolds guiding a spacecraft into Titan−centered Distant Retrograde Orbits (DROs), strategically selected for their scientific significance. Subsequently, Particle Swarm Optimization (PSO) is employed to fine−tune the insertion parameters, thereby minimizing ΔV. The results demonstrate that the proposed method allows for a reduction in ΔV of over 70% compared to conventional approaches like patched conics−based flybys (2.68 km/s vs. 9.23 km/s), albeit with an extended time of flight, which remains notably faster than weak stability boundary transfers. This paper serves as an interplanetary mission planning methodology to optimize spacecraft trajectories for the exploration of the Saturn−Titan system.

UGT1A1 and BLVRA allele and genotype variants in neonatal patients with hyperbilirubinemia in southern China

We explore the allele and genotype distribution of UGT1A1 and BLVRA variants in individuals affected by neonatal hyperbilirubinemia in southern China. Blood specimens were collected from 240 neonates: 126 cases of hyperbilirubinemia and 114 healthy controls. Serum levels of total protein, albumin, bilirubin (total and direct), urea nitrogen, creatinine, and other biochemical parameters were quantified using a biochemical analyzer. Nine UGT1A1 and five BLVRA genetic variants were genotyped using flight time mass spectrometry. The allele and genotype frequencies of these variants and their associations with neonatal hyperbilirubinemia were analyzed. The genotype frequencies of CC and CG for the UGT1A1 variant rs11888492 in the hyperbilirubinemia group were 90.48% and 9.52%, respectively (P = 0.001), in comparison with the control group. The C and G allele frequencies of rs11888492 in the hyperbilirubinemia group were 95.24% and 4.76%, respectively (P = 0.023). Similarly, in the hyperbilirubinemia group, the genotype frequencies for the UGT1A1 variant rs4148325 were 90.48% CC, 8.73% CT, and 0.79% TT (P = 0.001), with corresponding allele frequencies of 94.84% for C and 5.16% for T (P = 0.002). No notable distinctions were detected for other variants. Newborns carrying the CC genotype of rs11888492 exhibited higher total bilirubin (TBIL) levels than those carrying the GG genotype (P = 0.034), whereas newborns carrying the CC genotype of rs4148325 displayed higher TBIL levels than those carrying the CT genotype (P = 0.003). The presence of the G allele at rs11888492 was found to be significantly correlated with a decreased likelihood of developing neonatal hyperbilirubinemia (odds ratio [OR]: 0.363; 95% confidence interval [CI] 0.169–0.777). Furthermore, a substantial reduction in the risk of neonatal hyperbilirubinemia associated with the CT genotype of rs4148325 were revealed (OR = 0.242; 95% CI 0.102–0.574). Additionally, an inverse relationship was identified between TBIL concentration and the quantity of genetic variants. The UGT1A1 variants rs11888492 and rs4148325 are strongly associated with neonatal hyperbilirubinemia in southern China.

Deep Reinforcement Learning based running-track path design for fixed-wing UAV assisted mobile relaying network

This paper studies a fixed-wing unmanned aerial vehicle (UAV) assisted mobile relaying network (FUAVMRN), where a fixed-wing UAV employs an out-band full-duplex relaying fashion to serve a ground source-destination pair. It is confirmed that for a FUAVMRN, straight path is not suitable for the case that a huge amount of data need to be delivered, while circular path may lead to low throughput if the distance of ground source-destination pair is large. Thus, a running-track path (RTP) design problem is investigated for the FUAVMRN with the goal of energy minimization. By dividing an RTP into two straight and two semicircular paths, the total energy consumption of the UAV and the total amount of data transferred from the ground source to the ground destination via the UAV relay are calculated. According to the framework of Deep Reinforcement Learning and taking the UAV's roll-angle limit into consideration, the RTP design problem is formulated as a Markov Decision Process problem, giving the state and action spaces in addition to the policy and reward functions. In order for the UAV relay to obtain the control policy, Deep Deterministic Policy Gradient (DDPG) is used to solve the path design problem, leading to a DDPG based algorithm for the RTP design. Computer simulations are performed and the results show that the DDPG based algorithm always converges when the number of training iterations is around 500, and compared with the circular and straight paths, the proposed RTP design can save at least 12.13 % of energy and 65.93 % of flight time when the ground source and the ground destination are located 2000 m apart and need to transfer 5000bit/Hz of data. Moreover, it is more practical and efficient in terms of energy saving compared with the Deep Q Network based design.

Artificial Skin Based on Visuo‐Tactile Sensing for 3D Shape Reconstruction: Material, Method, and Evaluation

AbstractArtificial skin has shown great potential in robot perception and human healthcare. It provides multifunctional tactile sensing, including 3D shape reconstruction, contact feedback, and temperature perception, where the 3D reconstruction function is indispensable for dexterous hands in tactile cognition and interaction. Vision‐based tactile sensor (VTS) is an innovative bionic tactile sensor and supports high‐resolution, high‐precision, and high‐density tactile reconstruction compared with electronic tactile sensors. Considering the unique contribution of visuo‐tactile sensing to artificial skin, this review focuses on the 3D reconstruction techniques of the VTS. 3D reconstruction methods are classified into five categories based on sensing modalities, hardware categories, and modeling approaches: 1) photometric stereo, 2) binocular depth calibration, 3) optical flow, 4) deep learning, and 5) ToF (time of flight). In addition, the association and difference of reconstruction methods are analyzed from the hardware perspective, and the development and technological details of 3D reconstruction are summarized. On this basis, the challenges and development direction are discussed. This review can be viewed as a technology guide to provide references for interested researchers. Furthermore, it is expected to promote the extensive application of the VTS in artificial skins.

Fast neutron generation with few-cycle, relativistic laser pulses at 1 Hz repetition rate

Laser-driven deuterons generate neutrons with a mean energy of 2.5 MeV, through the 2H(d,n) fusion reaction in a deuterated polyethylene (dPE) tablet. The deuterium ions are accelerated by 12 fs, 21 mJ laser pulses interacting with a 0.2 µm thin dPE foil at a peak intensity of 1018 W/cm2. The laser was operated at 1 Hz repetition rate in bursts of 75 shots. The interaction was characterized and recorded for each laser shot. The ion spectra were measured in the forward and backward directions by Thomson ion spectrometers. Neutron events were detected by a time-of-flight (ToF) system consisting of four plastic scintillators positioned at various angles around the experimental chamber. The maximum cut-off energy of the forward accelerated protons and deuterons was close to 1.4 MeV and 1 MeV, while the mean values are 428 ± 63 keV and 433 ± 80 keV, respectively. Analysis of ToF distributions from 3128 shots resulted in an average yield of 1142 ± 59 neutrons per shot in the energy range of 1.5–4 MeV. The energy distribution of forward-directed neutrons peaks between 3 and 3.5 MeV. Angular dependence analysis showed a perpendicular minimum and a maximum along the deuteron beam, consistent with the expected distribution from the literature and our simulation results.

Accurate determination of matrix composition in Cd x Zn 1 − x O semiconductor material using MS-SIMS and ToF-SIMS methods

Abstract This study focuses on the method for determining the exact composition for Cd x Zn 1 − x O semiconducting material using secondary ion mass spectrometry. The calibration curve method is employed to establish a quantitative relationship between the intensity of secondary ions and the concentrations of elements in Cd x Zn 1 − x O thin films. Additionally, the study compares the relative sensitivity factors with the calibration curve method for determining the value of x in Cd x Zn 1 − x O . A comparison between the performances of Time of Flight and Magnetic Sector SIMS in analyzing Cd x Zn 1 − x O thin films is also presented. This approach aims to enhance the accuracy and reliability of quantitative analysis in SIMS for Cd x Zn 1 − x O thin films.

Visualization of cortical neoangiogenesis after combined revascularization surgery in moyamoya disease using silent MRA.

To investigate postsurgical indirect cortical neoangiogenesis in patients with moyamoya disease (MMD) using silent magnetic resonance angiography (MRA). We studied 44 patients with MMD (63 hemispheres) who were previously revascularized with combined bypass surgery (23 and 40 hemispheres in pediatric and adult patients, respectively). They underwent follow-up for postoperative bypass patency using time-of-flight (TOF)-MRA and silent MRA between January 2022 and December 2023. The mean duration from surgery to MRA was 8.5 years (range, 1.2-22.3 years). Two observers independently rated the revascularization as follows: 0 (near-complete signal loss or no signal); 1, poor (slightly visible donor arteries); 2, good (acceptable revascularization around the brain surface); and 3, excellent (good quality of revascularization with perfusion from the cortical surface into the middle cerebral artery). Silent MRA visualized indirect bypass significantly better than TOF-MRA (2.6 ± 0.7 and 1.4 ± 0.8) (P < 0.01). In silent MRA, the mean score of indirect bypass was significantly higher than that of direct bypass (2.6 ± 0.7 and 1.7 ± 1.0; P < 0.01) and indicated good indirect bypass development in both children and adults (91.3% and 85.0%; score ≥ 2). Children exhibited a higher rate of excellent indirect bypass patency than adults (73.9% and 55.0%; score 3). Poor bypass development in indirect bypass (8 hemispheres, mean age: 35.5 ± 17.5 years, mean follow-up period: 11.3 years) was significantly observed in male patients (P < 0.01). Silent MRA enables better precision in postsurgical visualization of indirect cortical neoangiogenesis during long-term follow-up and reveals indirect bypass development even in adult patients.

Mozart: A Mobile ToF System for Sensing in the Dark Through Phase Manipulation

Sensing in low-light and dark environments has a wide range of applications. However, existing sensing technologies suffer several major challenges, such as excessive noise and low resolution. In this work, we propose Mozart - a new mobile sensing system that leverages off-the-shelf Time-of-Flight (ToF) depth cameras to generate high-resolution and rich-in-texture maps for applications in dark scenarios. The design of Mozart is based on our key observation that the phase components of ToF measurements can be manipulated to expose texture information. Through in-depth analysis of the physical reflection model, we show that the textures can be exposed and enhanced using highly compute-efficient phase manipulation functions. Moreover, by exploiting the physics texture models, we propose an autoencoderbased unsupervised learning approach that can automatically learn efficient representations from phase components to generate high-resolution maps. We implemented Mozart on several Android smartphone models and an edge testbed with standalone ToF camera platforms for various applications in the dark. The results show that Mozart can work in real time and delivers significant improvement over existing sensing technologies. The demo video at https:// www.youtube.com/watch?v=L_sxyTZxIdU shows that Mozart offers a low-cost, highperformance sensing technology for nextgeneration applications in the dark.

First fossil mountain midges (Diptera, Deuterophlebiidae) and their evolutionary and ecological implication

We present here the first-ever fossil flies from the family Deuterophlebiidae. The recent adults have an exceptionally brief lifespan, with males surviving only two hours. Their distinctive features include a complete reduction of mouthparts, and wing venation characterized by a dense net of false veins replacing most of true veins. Due to this distinctive venation the phylogenetic position of Deuterophlebiidae was unclear, compounded by the absence of fossils that could shed light on the early development of these characters. Two new genera and species are described from Burmese amber, Protodeuterophlebia oosterbroeki Krzemiński, Krzemińska & Soszyńska, gen. et sp. nov. and Cretodeuterophlebia courtneyi, Krzemiński, Skibińska & Kopeć, gen. et sp. nov. They date back the age of the family to the mid-Cretaceous. Notably, the fossils reveal first false veins and reduced mouthparts, suggesting a short lifespan in these Cretaceous mountain midges. A comparative analysis of wing venation indicated the Hennigmatidae as a plausible ancestral group to the Deuterophlebiidae. A syninclusion of mayfly indicates the coexistence of these short-lived insects during the same flight period. This synchrony extends to their brief time in flight aligning with the flow of fluid resin. The occurrence of these simultaneous events is extremely low, emphasizing the significance of these findings.

Path Planning of an Unmanned Aerial Vehicle Based on a Multi-Strategy Improved Pelican Optimization Algorithm.

The UAV path planning algorithm has many applications in urban environments, where an effective algorithm can enhance the efficiency of UAV tasks. The main concept of UAV path planning is to find the optimal flight path while avoiding collisions. This paper transforms the path planning problem into a multi-constraint optimization problem by considering three costs: path length, turning angle, and collision avoidance. A multi-strategy improved POA algorithm (IPOA) is proposed to address this. Specifically, by incorporating the iterative chaotic mapping method with refracted reverse learning strategy, nonlinear inertia weight factors, the Levy flight mechanism, and adaptive t-distribution variation, the convergence accuracy and speed of the POA algorithm are enhanced. In the CEC2022 test functions, IPOA outperformed other algorithms in 69.4% of cases. In the real map simulation experiment, compared to POA, the path length, turning angle, distance to obstacles, and flight time improved by 8.44%, 5.82%, 4.07%, and 9.36%, respectively. Similarly, compared to MPOA, the improvements were 4.09%, 0.76%, 1.85%, and 4.21%, respectively.

Design of Nanosecond Pulse Laser Diode Array Driver Circuit for LiDAR

The pulse laser emission circuit plays a crucial role as the emission unit of time-of-flight (TOF) LiDAR. This paper proposes a nanosecond-level pulse laser diode array drive circuit for LiDAR, primarily aimed at addressing the issue of high-speed scanning drive for the laser diode array at the emission end of solid-state LiDAR. Based on the single pulse laser diode drive circuit, this paper innovatively designs a circuit that includes modules such as a boost circuit, linear power supply, high-speed gate driver, GaN field-effect transistor, and pulse narrowing circuit, realizing an 8-channel laser diode array drive circuit. This circuit can achieve a pulse laser array drive with a single channel operating frequency of greater than 100 kHz, an output pulse width of less than 5 ns, a peak power greater than 75 W, and a channel switching time that does not exceed 1 μs. A field programmable gate array (FPGA) is used to control the operation of this circuit and perform a series of performance tests. Experimental results show that this circuit has a high repetition rate, large output power, a narrow pulse width, and fast switching speeds, making it highly suitable for use in the optical emission module of solid-state LiDAR.

Three six-degree-of-freedom dynamic descent convex optimization guidance methods

A key technology for soft landing of powered descent of aircraft is to solve the six-degree-of-freedom powered descent guidance problem in real time, which can be described as the fuel-saving trajectory optimization problem under multiple constraints. Three optimization models are established by selecting three independent variables: flight time, time substitution variable, and trajectory height. The original trajectory optimization problem is transformed into a solvable form of sequential convex optimization for iterative solution, forming three online guidance methods. The three guidance methods are compared in terms of convergence, real-time performance, optimality, and solution accuracy. The results show that all three guidance methods can solve the six-degree-of-freedom powered descent problem. The guidance method with flight time as the independent variable has the shortest calculation time and the least fuel consumption, but the powered descent flight time needs to be determined in advance. The guidance methods based on the other two types of independent variables can optimize the powered descent flight time, but they are all suboptimal solutions, and the calculation time is significantly increased. The solution accuracy of the three methods is similar under the same number of discrete points. If sequential convex optimization is used as the online guidance scheme for powered descent, how to determine the optimal flight time, approach the optimal solution for fuel, and further shorten the calculation time still needs to be further studied.

Spaceborne particle identification platform and its application based on convolutional neural network

The identification of particle types in space radiation particle detection is a problem of great concern in scientific research and engineering applications. At present, the particle types are mainly identified by the difference in energy deposition of particles in the sensor, the difference in waveform in the sensor, the flight time and energy of particles, and the difference in the deflection path of particles in the electric field, such as traditional telescope detectors, particle pulse waveform analysis, flight time TOF system and electrostatic analyzer. However, the current on-orbit particle identification logic is relatively simple and the identification accuracy is limited. Convolutional neural networks have powerful target classification capabilities and are good at capturing and extracting target feature details, which can improve the accuracy of particle energy measurement and identification. Based on the environment commonly used in space environment detection payloads, this paper proposes a method for building an on-orbit convolutional neural network particle identification platform to achieve particle type identification. The platform first constructs a multi-dimensional input data set, completes the model training and weight derivation with the help of the software platform, and completes the waveform inference and data set expansion through the hardware platform. The established identification platform is used to train and test the neutron and gamma waveform data obtained in actual tests, and the accuracy of the identification of the software and hardware platforms is analyzed, completing the verification of the platform. The establishment and application of this platform provides a new idea and method for particle measurement and identification in future space environment detection, and has strong engineering practice significance.

Performance evaluation and energy management strategy of drone methanol reforming fuel cell hybrid power system: A case study

The performance of hydrogen production by methanol steam reforming and high temperature proton exchange membrane fuel cells on drone lacks the comparison of experimental data, resulting in the overall performance improvement of the system is difficult to evaluate. Therefore, the energy management strategy of methanol reforming high temperature proton exchange membrane fuel cell hybrid power system and state machine is designed and optimized. The performance of this system is analyzed and compared with the flight experiment data of a proton exchange membrane fuel cell hybrid UAV. The results show that the SOC of the system can be stabilized between 70 and 80 during the long cruise phase. The test results show that the maximum hydrogen storage mass density of the hydrogen supply system is increased by 67.6%. The flight time of the system under economic cruise flight of the drone is improved by 29.1%–44.9% with different quality of fuel. Under the maximum flight speed of a certain wind speed, the system can meet the flight requirements and increase the flight time by 31.9% and 17.6% respectively under the wind speed of 1.5 m/s and 4 m/s. Its flight time has been greatly improved, and it has great potential as a drone system.

Assessing the deep learning based image quality enhancements for the BGO based GE omni legend PET/CT

BackgroundThis study investigates the integration of Artificial Intelligence (AI) in compensating the lack of time-of-flight (TOF) of the GE Omni Legend PET/CT, which utilizes BGO scintillation crystals.MethodsThe current study evaluates the image quality of the GE Omni Legend PET/CT using a NEMA IQ phantom. It investigates the impact on imaging performance of various deep learning precision levels (low, medium, high) across different data acquisition durations. Quantitative analysis was performed using metrics such as contrast recovery coefficient (CRC), background variability (BV), and contrast to noise Ratio (CNR). Additionally, patient images reconstructed with various deep learning precision levels are presented to illustrate the impact on image quality.ResultsThe deep learning approach significantly reduced background variability, particularly for the smallest region of interest. We observed improvements in background variability of 11.8%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document}, 17.2%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document}, and 14.3%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} for low, medium, and high precision deep learning, respectively. The results also indicate a significant improvement in larger spheres when considering both background variability and contrast recovery coefficient. The high precision deep learning approach proved advantageous for short scans and exhibited potential in improving detectability of small lesions. The exemplary patient study shows that the noise was suppressed for all deep learning cases, but low precision deep learning also reduced the lesion contrast (about −30%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document}), while high precision deep learning increased the contrast (about 10%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document}).ConclusionThis study conducted a thorough evaluation of deep learning algorithms in the GE Omni Legend PET/CT scanner, demonstrating that these methods enhance image quality, with notable improvements in CRC and CNR, thereby optimizing lesion detectability and offering opportunities to reduce image acquisition time.

Imaging research on damage localization of concrete structure based on Lamb wave and Bayesian fusion algorithm

Concrete structures are critical materials in the field of industrial construction and are susceptible to invisible damage under the influence of a variety of factors. The method of increasing the number of sensors is usually used to obtain high-precision damage detection, but it increases the experimental cost and signal processing time, so it is especially important to perform high-precision and low-cost non-destructive testing for it. The article uses Bayes to combine the elliptical trajectory method with a probabilistic damage imaging algorithm, and utilizes the algorithm to achieve higher accuracy damage localization imaging results with a smaller number of sensors. The wavelet transform is used to calculate the time of flight to obtain the localization results of the elliptical trajectory method, the signal damage factor is calculated based on the amplitude information to obtain the localization results of the probabilistic damage imaging, the probability distribution of the damage location is obtained using Bayesian, and the damage localization results are obtained by solving the problem through the Markov chain Monte Carlo method. A dense sensor array based and probabilistic imaging algorithm is also used to localize the damage to the material and the results are compared with the results of the Bayesian fusion algorithm. The results show that the Bayesian fusion algorithm can obtain higher accuracy of damage localization with a significant reduction in the number of sensors, and at the same time greatly reduces the number of signals, simplifies the experimental process, and has high practical value.

Autonomous trajectory planning method for aircraft based on ripple diffusion algorithm

Aiming at the problem that route optimization in dynamic continuous airspace (non-discrete road network) is difficult to ensure optimality and has low computational efficiency, a non-circular ripple diffusion algorithm is proposed. Firstly, considering the influence of time-varying airflow and obstacle area in dynamic airspace, the airspace wind speed model, route network and dynamic network weight model are established, and 6 airspace environments and 4 road network structures with the influence of time-varying airflow and obstacle area are constructed. Then, the effects of traditional dynamic path optimization algorithm, co-evolutionary path optimization algorithm and non-circular ripple diffusion algorithm on route optimization are compared in 1000 experiments, taking the optimized path length, flight time and calculation time as indicators. The simulation results show that the traditional dynamic path optimization algorithm has the worst effect under the three indicators, and the calculation time of non-circular ripple diffusion algorithm is significantly less than that of the other two algorithms, which effectively improves the engineering calculation efficiency.

Delamination inspection in composite laminates with S0 Lamb wave using flexible macro-fiber composite transducers

Abstract Delamination in composite laminates poses a serious threat to structural integrity in aerospace applications due to its potential to cause structural failure. Effective structural health monitoring (SHM) methods are needed to identify such damage. The objective of this paper is to measure and localize delamination in a glass fiber reinforced polymer (GFRP) cross-ply composite laminate based on the S0 Lamb wave detection method, using the Macro-Fiber Composite (MFC) as both actuator and receiver. To analyze the interaction behaviors between the excited S0 Lamb wave and delamination, the finite element models were established to simulate the Lamb wave propagation in composites, incorporating the local stiffness reduction method to model the delamination region. The simulation results indicate that the interaction behaviors between the excited S0 wave and the delamination occur mainly at the end of the delamination, and the generated new wave packets in the received signals can be used to identify the delamination. Furthermore, the S0 wave reflected from the end edge of delamination was used to localize the delamination. Reflection coefficients were found to vary significantly with delamination depth, and the delamination's longitudinal position was accurately localized using Time-of-Flight (ToF) extraction. Finally, a pitch-catch experiment with MFC transducers was conducted to detect an artificial delamination, demonstrating that it is a promising approach to inspect delamination accurately with end-edge reflected waves.