Abstract:Heat flux density, as a key parameter for evaluating the thermal environment in hypersonic flight tests, plays an extremely important role in assessing the performance of aircraft and ensuring flight safety. Focus on overview of the main heat flux measurement methods currently used in aerospace hypersonic tests, including contact sensors such as thin-film and coaxial thermocouples, as well as optical methods such as infrared and phosphorescence. Elaborates on the basic principles, technical features, and application practices of each method in domestic and international significant hypersonic tests. Summarizes the applicability, advantages, and limitations of different heat flux measurement techniques in complex scenarios and conditions of flight tests, point out the key engineering and technical challenges that still need to be addressed. Additionally, it analyzes the challenges faced by heat flux measurement technologies in medium and long-duration hypersonic flights and proposes suggestions for future research directions, providing a certain reference for subsequent heat flux density measurements in flight tests.

Abstract:This paper presents a flight-control self-destruction system to mitigate sensitive data leakage when unmanned aerial vehicles are captured. The system employs an over-current-driven physical damage mechanism and an adaptive sliding-window dynamic decision algorithm that integrates statistical features with first-order difference trend analysis. Multi-stage triggering criteria, false-decision tolerance, and power-consumption optimization are combined with displacement monitoring via a linear Hall-effect sensor and encrypted command verification to ensure accurate detection of unauthorized disassembly. A TL494-based over-current module delivers a 40 V/20 A high-energy pulse, irreversibly damaging the flight controller′s core circuits. The experimental results show that the proposed method is effective in complex environments such as strong electromagnetic interference and temperature changes. Its execution time still reaches about 18.2 ms response speed and 2% false trigger rate. Although response speed is 7 to 12 ms slower than fixed-threshold and moving-average algorithms, the false-trigger rate improves by 31% and 21.5%. Compared with existing schemes, the proposed system offers low cost, high reliability, and practical protection for UAV data security.

Abstract:To meet the demand for high-precision testing of total temperature in high-temperature, high-speed airflows within aeroengines, this paper employs fluid-structure interaction numerical simulation methods to systematically investigate the flow heat transfer and temperature measurement error characteristics of a shielded thermocouple under seven velocity conditions ranging from 0.2 to 0.8 Ma and six temperature conditions from 700℃ to 1 200℃. Results indicate that the shielding cover exhibits significant stagnation effects, reducing flow velocity at the measurement tip by over 80%. Error analysis reveals that all error terms increase with rising Mach number, with growth rates following the sequence: radiation error > thermal conduction error > velocity error. At low Mach numbers (Ma≤0.3), thermal conduction error dominates. However, as Mach number increases, radiation error becomes significantly more influential and emerges as the primary error source. Elevated temperatures further exacerbate the impact of radiation error. Overall, thermal conduction and radiation errors collectively account for over 93% of the total error, constituting the key factors affecting measurement accuracy. After applying an empirical radiation error correction formula, the steady-state error decreased from 17.97 K to 0.64 K, and the overall temperature recovery coefficient improved to above 0.92, significantly enhancing measurement precision.

Abstract:To meet the airworthiness certification requirements for hail ingestion tests of aircraft engines and address the scarcity of research on multi-hail simultaneous ejection devices in China, a single-chamber multi-barrel hail ejection device was designed. The influence of the device′s structural parameters on the hail ejection process was analyzed through simulation, and experimental verification was carried out. The results show that the device enables simultaneous ejection of multiple hailstones with a speed error ≤±1.5%, position dispersion ≤±5 mm, and synchronization time ≤35 ms, all of which comply with airworthiness regulations such as CCAR33.78, and the device also features stable performance and good repeatability. This study provides key equipment support for aircraft engine hail ingestion tests and validates the effectiveness of the single-chamber multi-barrel design in ensuring synchronization and accuracy control.

Abstract:The T(0,1) torsional guided wave is widely employed in pipe defect detection due to its non-dispersive characteristic. This paper presents a novel insertable torsional guided wave electromagnetic acoustic transducer, offering advantages of non-contact operation, simple structure, and high signal-to-noise ratio. The transducer primarily comprises arc-shaped coils, race-track shaped permanent magnets, and a support skeleton, generating torsional guided waves in the pipe based on the Wiedemann effect. Numerical simulations were first conducted to model the transducer′s axial dynamic magnetic field and circumferential static bias magnetic field, verifying the feasibility of torsional wave generation and investigating the influence of pipe bend radius on torsional wave propagation. Subsequently, utilizing the proposed T(0,1) torsional guided wave transducer combined with frequency-wavenumber analysis method, high-sensitivity detection of axial cracks in bent pipes and pitting defects in straight pipes was achieved. Experimental verification demonstrates that the transducer can effectively detect axial cracks 3 mm in length within bent pipes and pitting defects 0.4 mm in diameter within straight pipes at an excitation frequency of 0.32 MHz.

Abstract:This paper proposes a theory of parafoil flight trajectory tracking control based on switching systems, which adopts a control method of switching between linear active disturbance rejection control and proportional integral derivative controller. The linear active disturbance rejection control has fast response speed and strong anti-interference ability, and the proportional integral derivative controller relies on error signals to achieve basic control. Switching control is adopted to track and control the azimuth angle of a given reference trajectory; in the switching system, it is determined whether to switch to the corresponding controller based on whether the given reference trajectory is a straight flight, whether the given turning radius of the flight trajectory changes, and so on. The simulation results show that compared with any single control, the switching system has more advantages and better tracking control performance on the flight trajectory.

Abstract:Aiming at the difficulty in fine segmentation of irregular boundaries in coastal remote sensing images, this paper proposes an Asymmetric Multi-path Decoding Network for Coastline Segmentation (AMDNet). Taking Deeplabv3+ as the backbone network, the network uses EfficientNet-B0 as the feature extractor to significantly reduce the computational load of the network. Additionally, the D-LKA module is introduced into the improved ASPP to add extra offsets for adjusting the sampling positions of standard convolution, allowing the convolution kernel to flexibly adjust the sampling grid. Combined with DUpsampling technology to achieve high-precision restoration during the upsampling process, the accuracy of image segmentation is improved. The accuracy, sensitivity, Dice and Jaccard of the AMDNet model on the Aerial photo-maps dataset reach 96.77%, 93.03%, 90.42% and 86.67% respectively, showing a significant performance improvement.

Abstract:To address the challenges of high cost, vulnerability to interference, and system complexity associated with traditional positioning methods in UAV swarm flight within complex environments, this paper proposes a monocular vision measurement method based on companion flight imagery. By constructing a ″Rigid Baseline-to-Image Ratio Visual Ranging Model″, the wingtip line is used as a geometric benchmark to calculate relative position through optical projection transformation, while incorporating carrier attitude information for dynamic correction. Test results demonstrate that the ground simulation achieves 3D measurement errors of less than 2 cm within a 2 to 7 meter range. Flight tests within the operational envelope (X: 13 to 30 m, Y: 0 to 2.5 m, Z: 0 to 4 m) show root mean square errors better than 0.66 m, 1.16 m and 0.78 m in the three directions respectively, with a processing speed of 20 fps and stable performance across various formation configurations. This lightweight vision measurement technology effectively resolves the conflict between feature extraction quality and real-time performance for moving targets, providing a reliable technical solution for autonomous coordination of UAV swarms in complex electromagnetic environments.

Abstract:In the field of airborne measurement and control, the analog output of sensors is often limited by the influence of signal transmission distance and electromagnetic interference, resulting in signal distortion and accuracy reduction, which is difficult to meet the needs of high-precision data acquisition in test flight tests. In addition, for signals that change relatively slowly such as temperature and pressure, the traditional point-to-point multi-wire connection method brings problems such as complex wiring, high installation costs, and limited system flexibility. In order to solve the above challenges, this paper proposes a digital sensor acquisition technology scheme based on PowerBus bus. This solution uses the low-voltage carrier technology and two-wire connection characteristics of the PowerBus bus, combined with the concept of digital conversion of sensors, to build a high-performance acquisition system suitable for slowing signals (such as temperature and pressure). The acquisition system consists of a PowerBus master (bus network node) and a PowerBus slave station (digital sensor). The slave station is responsible for the acquisition, conditioning and high-precision analog-to-digital conversion of the front-end (including platinum resistors, thermocouples, and pressure sensors), and transmits the digitized data through the PowerBus bus. The master is responsible for managing the PowerBus network, receiving data from each slave, and encapsulating and uploading the data. The experimental results show that the system can achieve the internal sampling frequency of 1 ksps and the sampling accuracy of 16 bit effective bits, and realize stable and reliable multi-node sensor data transmission under the rate limit of 9 600 bps of PowerBus bus, which verifies the feasibility and efficiency of the scheme in bandwidth-limited environments such as airborne testing.

Abstract:In view of the large-scale sensor data collection and extremely high time synchronization accuracy requirements in flight tests and aero-engine tests, this paper designs and implements a synchronous acquisition system based on networked sensors. The system utilizes a bus-based network architecture, integrating a unified trigger mechanism within nodes based on a shared clock with high-precision time synchronization across nodes based on IEEE 1588. This enables multi-level, high-precision data synchronization. By integrating an enhanced time synchronization algorithm based on IEEE 1588 into the bus-based network, nanosecond-level synchronization accuracy is achieved across the entire network. Combined with the enhanced time synchronization algorithm, nanosecond-level synchronization accuracy is achieved across the entire network. Through OMNeT++ simulation and FPGA hardware test verification, the results show that the scheme still has excellent synchronous acquisition performance in large-scale distributed sensor networks, and can meet the stringent requirements for microsecond-level synchronous acquisition in flight tests. This study provides a theoretical basis and a practical path for constructing a highly reliable, multilevel large-scale sensing system.

Abstract:Object detection models are markedly vulnerable to adversarial patches, posing serious safety risks to applications such as autonomous driving and security surveillance. Although transfer-based black-box attacks have made progress, they often suffer from poor cross-model transferability and uneven suppression across multi-scale detection heads. To address these issues, we propose MSBR for adversarial patch attacks. During patch training, MSBR explicitly regularizes the variance of confidence outputs across different detection scales, thereby enforcing consistent suppression of targets at multiple scales, mitigating scale-wise imbalance, and substantially improving cross-model transferability. Experiments on several mainstream detectors show that our method maintains strong attack success rates while outperforming representative approaches (e.g.T-SEA) in black-box transfer performance, demonstrating the practical effectiveness of MSBR. This work provides a new perspective for designing adversarial patch attacks against complex multi-scale detection architectures.

Abstract:A new type of ultra-high temperature probe at the aero-engine afterburner outlet has been independently developed to address the problem of difficult measurement of high-temperature airflow parameters at the outlet of aircraft engine afterburner combustion chambers. The ultra-high temperature probe solution is composed of a new type of ultra-high temperature resistant CMC-SiC silicon carbide ceramic matrix composite material as the main body，with a tungsten tube as the protective sleeve of embedded platinum rhodium thermocouple. We conducted preliminary verification tests such as airtightness and ablation tests on the main material of the probe，tensile tests on high-temperature bonding adhesive，and fatigue vibration tests on the entire probe. Finally，the ultra-high temperature probe was installed and passed the ground bench test inspection. The experiment results show that the ultra-high temperature probe can work normally under the full boost state of the engine，and the highest temperature measured at the outlet measuring point of the boost combustion chamber under full boost state is 1 680℃，which is comparable to the theoretical calculation value. The successful application of ultra-high temperature probe has improved the level of ultra-high temperature measurement for aviation engines，providing technical support for the improvement of flight test methods and design improvements for subsequent aviation engine afterburner combustion chamber flight tests.

Abstract:This paper designs a power distribution control unit for airborne test systems based on solid-state power controller technology, tailored to meet the power distribution control requirements of complex airborne test systems. The unit features remote power monitoring and intelligent power distribution protection capabilities. The adoption of miniaturized and modular design ensures flexible expansion of the power distribution scheme. Each control unit is equipped with 9 SSPC channels, which can be independently connected to the circuit according to the testing requirements of the aircraft. Intelligent control and management, such as power protection and remote switching, are carried out by taking advantage of the programmable feature of SSPC. With theoretical demonstration and practical verification, this power distribution control unit can be applied to various airborne environments. The design can significantly enhance the efficiency and quality of flight testing work and is of great significance in increasing the fault tolerance, reducing errors, and improving maintenance efficiency.

Abstract:Chinese calligraphy has a long and rich history, with hard-pen calligraphy bearing both artistic and practical significance. To address the decline in hard-pen handwriting ability caused by the widespread use of electronic devices, this paper proposes a multi-feature hard-pen calligraphy teaching mode based on force feedback, which integrates font style, stroke order, and writing pressure. Specifically, a Dense-CycleGAN model based on contrastive learning is developed to generate calligraphy font libraries in different styles. The stroke order of Chinese characters is standardized using the Hungarian algorithm. Furthermore, a mapping model from stroke width to writing pressure is constructed based on data collected via force feedback devices. Experimental results on five font styles show that the proposed model achieves an average Structural Similarity Index Measure (SSIM) of 0.587 in character generation, outperforming the traditional CycleGAN. The stroke order standardization yields a Dynamic Time Warping (DTW) score of 0.044 and an average cosine similarity of 0.998, indicating high accuracy. In user evaluation experiments, the writing guidance and teaching assistance received scores of 4.5/5 and 4.1/5, respectively, validating the practicality and applicability of the proposed mode. This writing mode faithfully reproduces the hard-pen calligraphy process and enables instruction that comprehensively considers font style, stroke order, and writing pressure, offering a novel integrated strategy for calligraphy education.

Abstract:High-precision tidal level data serves as a critical foundation for marine scientific research and engineering applications. With the expansion of modern marine activities, the demand for offshore long-distance tidal level information continues to grow. To further advance the acquisition of offshore high-precision tidal level data, this study conducted feasibility testing, accuracy research, and attitude compensation impact analysis for single-Beidou buoy tidal level measurement based on network RTK technology. Experimental results indicate: regarding elevation, incorporating attitude compensation effectively reduces errors and enhances measurement accuracy. Without attitude compensation, the maximum absolute error in tide level measurement was 4.10 cm, with an average deviation of 1.34 cm and a standard deviation of 1.54 cm, achieving an accuracy of 1.96 cm. With attitude compensation applied, the maximum absolute error decreased to 3.30 cm, the average deviation reduced to 1.14 cm, the standard deviation decreased to 1.44 cm, and the accuracy improved to 1.75 cm. Regarding phase, the overall phase lag variation remained within 4°. Adding attitude compensation showed no significant effect on improving phase shift. Therefore, a single-Beidou buoy based on network RTK demonstrates certain feasibility for tide level measurement. In fields such as smart waterway construction and hydrographic surveying, a single-Beidou tide gauge buoy based on network RTK holds promising application prospects.

Abstract:To meet the requirements of compactness and miniaturization for double-pulse test platforms, this paper proposes a design of a toroidal air-core reactor to address the severe magnetic field leakage issue associated with traditional solenoid and dry-type air-core reactors. The reactor is wound with Litz wire and optimized based on the equivalent current loop theory. The inductance value and parasitic parameters of the reactor are calculated through theoretical analysis and electromagnetic field simulation software such as Ansys, and its insulation performance, thermal stability, and dynamic stability are verified. Based on its dual-layer winding configuration, the reactor offers three selectable values to accommodate diverse testing scenarios. The results show that the toroidal air-core reactor has high energy storage density, low magnetic field leakage, and good stability, with its energy storage density being 28.389 times higher than that of traditional solenoid reactors. The reactor performs well in double-pulse testing. The conclusion indicates that the proposed reactor design meets the developmental requirements of double-pulse test platforms and can serve as an important technical means for their compact and miniaturized development.

Abstract:Detection of surface defects in steel materials is a key link in ensuring the quality of products in the manufacturing industry. Manual visual inspection and basic optical detection methods suffer from low efficiency and high miss detection rates, and the limited samples of existing datasets restrict the model′s generalization ability. Therefore, this paper proposes a lightweight steel surface defect detection method that integrates LS-DCGAN data augmentation with an improved YOLOv8 model. Firstly, to address the issue of insufficient sample diversity in the NEU-DET dataset, we use an LS-DCGAN generative adversarial network for data augmentation, effectively supplementing the morphological features and distribution characteristics of defect samples. Secondly, we conduct triple optimization on the YOLOv8 model to propose the SPH-YOLO detection algorithm: reconstructing the C2f module structure to enhance feature extraction capabilities, embedding an attention mechanism to improve focus on defect areas, and designing a multi-level feature fusion pyramid for cross-scale information interaction. Finally, we validate the improved model on the enhanced NEU-DET and GC10-DET datasets. Experimental results show that the improved model achieves a 3.2% increase in mAP@50%, a 28.5% reduction in parameter count, and a 12.3% decrease in computational load. Furthermore, the improvement method exhibits strong generalization ability, effectively balancing the lightweight nature of the detection model and its detection performance.

Abstract:With the rapid development of distributed energy resources, accurate prediction of their output has become a critical component in the reliability assessment of distribution networks. To enhance the accuracy of such assessments, this paper proposes a reliability evaluation method for active distribution networks that integrates VMD-QRCNN-BiLSTM-based forecasting with DFT-MP-DBN modeling. First, the original time series data of wind power, solar power, and load are decomposed into intrinsic mode components using Variational Mode Decomposition (VMD). Then, a Quantile Regression Convolutional Neural Network (QRCNN) is employed to extract the temporal features, and a Bidirectional Long Short-Term Memory (BiLSTM) network is used to model each variable and generate accurate forecasts. These predicted values are then input into a Dynamic Fault Tree (DFT), where a Continuous-Time Markov Process (MP) is used to compute the state transition rate matrix. Finally, a Dynamic Bayesian Network (DBN) is applied to capture the temporal dependencies among system states and incorporate observed or control variables. Case studies based on the IEEE RBTS Bus 2 system show that the proposed method achieves superior reliability performance, with SAIFI, SAIDI, AENS, and ASAI values of 0.231 times/customer/year, 3.496 hours/customer/year, 17.465 kWh/year, and 99.943%, respectively—significantly outperforming traditional approaches. These results validate the effectiveness and advantages of the proposed method in improving the precision and efficiency of distribution network reliability assessments.

Abstract:To address the limitation of error performance in Massive MIMO detection algorithms under high-order modulation scenarios, a novel detection network is proposed. This network is constructed based on a projected gradient descent framework that approximates the maximum likelihood solution through an iterative structure, which is then implemented using a neural network. In each network module, parameters are first learned through a neural network, followed by a nonlinear transformation using a designed normalized multi-segement activation function to enhance the network′s mapping capability under high-order modulation. Finally, a denoiser is employed to eliminate estimation errors and channel noise. Furthermore, to tackle the issue of accuracy degradation with increased network depth, residual connections are introduced between network modules. Simulation results show that when the number of transmitting and receiving antennas of the system is 64×32 and the signal-to-noise is 16 dB, the bit error rate of the proposed detection network is close to 10-4, and the bit error rate is reduced by an order of magnitude compared with other detection algorithms, showing the bit error performance close to the optimal detection algorithm, and has good robustness.

Abstract:To address the issues of insufficient feature extraction and inadequate prediction accuracy of single models in wind turbine condition forecasting, this study proposes a wind turbine operational condition prediction method that integrates the CatBoost algorithm with the Long Short-Term Memory network (LSTM). Firstly, based on the wind turbine sensor features and temporal features, the SVFE (a feature extraction method, assume its full name is known in the specific context) and MVFE (another feature extraction method, assume its full name is known in the specific context) methods are employed for cross-fusion to generate global composite features. Additionally, feature dimension reduction is achieved by incorporating grey relational analysis improved with the entropy weight method. Secondly, the Sparrow Search Algorithm (SSA) enhanced by chaotic mapping, termed CSSA, is introduced to conduct global optimization of the hyperparameters of the LSTM model, enabling adaptive screening and precise determination of the optimal parameter combination. Finally, the CatBoost model and the optimized LSTM model are deeply fused using an optimal weighted combination strategy to enhance prediction accuracy and model generalization capability. Taking the wind turbines of a phosphorus chemical enterprise in Yichang, China, as an example, the proposed CSSA-CatBoost-LSTM wind turbine condition prediction method was validated. The validation results demonstrate significant improvements in both accuracy and reliability of this method.

Abstract:To address the challenge of achieving both wide-range coverage and high-precision delay in conventional laser echo simulators, we designed a fully digital, high-precision delay signal control system with coarse-fine tuning capability, implemented on an FPGA. The system employs a dynamic phase adjustment strategy that integrates a clock counter with a mixed-mode clock manager (MMCM). Coarse delay adjustment is performed using a 250 MHz system clock counter, while fine delay compensation is realized through MMCM-based phase interpolation with 17.857 ps resolution steps. Experimental results demonstrate that, within a simulation range of 300 m to 30 000 m, the delay accuracy is better than 6 ps, corresponding to a distance resolution of 0.01 m. In actual measurements, the delay accuracy exceeds 1.2 ns, equivalent to a distance resolution of 0.18 m. This system achieves precise delay control across a wide range, offering a robust and reliable solution for performance evaluation of pulse laser rangefinders.

Abstract:To overcome the limitations of existing Transformer-based super-resolution models, which rely on self-attention mechanisms and face challenges in computational complexity and local detail capture, an optimized lightweight super resolution network is proposed. The network aims to efficiently utilize global, non-local, and local features for enhanced reconstruction. First, a spatial-frequency feature aggregation layer, incorporating dynamic strip attention and unbiased dynamic frequency awareness, is used to capture global and non-local features, ensuring that the network can effectively recover image feature. Then, to ensure the restoration of image details, a local detail enhancement layer is constructed to encode local context and perform channel mixing. Finally, multiple spatial-frequency feature modulation blocks progressively extract features and perform up-sampling reconstruction to produce the final super-resolution image. The proposed algorithm was benchmarked on five public super-resolution datasets, including Set14, BSD100, and Urban100. Under the ×2 reconstruction, it reduces FLOPs by 24.2% and requires a smaller training dataset compared with ShuffleMixer, another lightweight super-resolution network, while attaining gains of 0.54 dB in PSNR and 0.0055 in SSIM on the Urban100. Experiments show that the proposed network excels in lightweight super-resolution tasks, achieving a good balance between performance and complexity.

Abstract:Computer vision plays a crucial role in the field of intelligent perception. Existing methods for psychological state perception are typically limited to single tasks such as facial expression recognition or remote photoplethysmography, making it difficult to achieve collaborative perception of multidimensional features. Additionally, approaches that integrate multimodal physiological signals face high computational costs. To address these challenges, this paper proposes a non-contact psychological state perception method based on multi-task rotation learning. The proposed approach utilizes a multi-task model to process facial video, simultaneously performing three tasks: rPPG heart rate signal extraction, emotional valence and arousal prediction, and psychological state classification. Experimental results show that the model achieves an average absolute error of 3.78 for rPPG heart rate signal extraction, prediction accuracies of 97.47% and 96.75% for emotional valence and arousal, respectively, and a classification accuracy of 97.42% for psychological state. This method provides an efficient multi-task processing solution for non-contact psychological state perception, offering significant theoretical and practical value.

Abstract:To solve the problems of insufficient time-frequency feature representation ability and insufficient neural network feature learning in the existing radar human behavior recognition, a human behavior recognition method based on radar time-frequency feature extraction and CBAM-MFResNet is proposed. In the time-frequency feature extraction section, radar echo signals are processed, the distance window function is used to constrain the spectral energy diffusion problem in the behavioral signal, and along the slow time dimension, an adaptive wavelet threshold-Chebyshev window function co-processing mechanism is constructed to suppress clutter interference. Micro-Doppler time-frequency diagrams are obtained by time-frequency analysis. In the network model building section, a CBAM-MFResNet model for behavior recognition is constructed, the lightweight convolutional attention mechanism is introduced into the residual neural network to enhance the representation of key features; and an efficient parallel multi-scale feature learning module was designed to learn diverse feature information to reflect the feature differences of different behaviors to the greatest extent. Finally, the fused features are input into the fully connected layer for classification. Experimental results show that the proposed model and clutter filtering algorithm can effectively improve the accuracy of the recognition system, and the average recognition accuracy of different human behaviors reaches 98%.