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Zhang Yuqin, Jiang Hongna, Zhang Xiaofei
2025,48(24):1-9, DOI:
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.
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Niu Wei, Zhang Yingzi, Kang Changsheng, Zhang Yizhou
2025,48(24):10-18, DOI:
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.
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2025,48(24):19-26, DOI:
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.
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Xu SiqiXue, Wenpeng, Jin Liqiang
2025,48(24):27-33, DOI:
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.
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2025,48(24):34-42, DOI:
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.
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2025,48(24):43-50, DOI:
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.
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2025,48(24):51-58, DOI:
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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.
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2025,48(24):59-67, DOI:
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.
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Yan Rong, Li Ludan, Yin Chuan, Wu Jinxing
2025,48(24):68-79, DOI:
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.
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Gu Shipeng, Yu Tianxiang, Yin Chuan, Zhang Lei
2025,48(24):80-88, DOI:
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.
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Xie Jiale, Zhao Yuxi, Zeng Nianyin, Wang Ruo
2025,48(24):89-96, DOI:
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.
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2025,48(24):97-102, DOI:
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.
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2025,48(24):103-109, DOI:
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.
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Zhang Huixin, Zhao Qirong, Xiong Min
2025,48(24):110-120, DOI:
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.
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Jiang Xiaoyu, Zhou Wenqing, Shao Yi
2025,48(24):121-127, DOI:
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.
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2025,48(24):128-137, DOI:
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.
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Xu Jiangmiao, Cao Shuang, Guan Haiyan
2025,48(24):138-147, DOI:
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.
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2025,48(24):148-158, DOI:
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.
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Kang Xiaofei, Jing Yiyang, Guo Hanyu
2025,48(24):159-166, DOI:
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.
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Sun Jiadong, Li Ziheng, Chen Deji, Shi Pei
2025,48(24):167-176, DOI:
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.
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2025,48(24):177-185, DOI:
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.
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Gu Yuzhou, Li Jiao, Guo Aiying, Wu Haochen, Lu Junyu
2025,48(24):186-194, DOI:
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.
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2025,48(24):195-203, DOI:
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.
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Yan Linglan, Chang Jun, Zhao Nan, Hu Tao
2025,48(24):204-212, DOI:
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%.
Volume 48, 2025 Issue 24
Flight Test Measurement and Technology
Research&Design
Theory and Algorithms
Information Technology & Image Processing
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Xue Xianbin, Tan Beihai, Yu Rong, Zhong Wuchang
2024,47(6):1-7, DOI:
Abstract:
Urban intersections are accident-prone sections. For intelligent networked vehicles, it is very important to carry out risk detection and collision warning during driving to ensure the safety of driving. This paper proposes a traffic risk field model considering traffic signal constraints for urban intersections with traffic lights, and designs a three-level collision warning method based on this model. Firstly, a functional scenario is constructed according to the potential conflict risk points of urban intersections, and the vehicle risk field model is carried out considering the constraint effect of traffic signal. In order to solve the problem of collision warning, a three-level conflict area is proposed to be divided by the index, and the collision risk of the main vehicle is measured according to the position of the potential energy field around the main vehicle by calculating the corresponding field strength around the main vehicle. The experimental results show that the designed model can accurately warn the interfering vehicles entering the potential energy field of the main vehicle, the warning success rate can reach 100%, and the false alarm rate is only 3.4%, which proves the reliability and effectiveness of the proposed method.
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Wei Jinwen, Tan Longming, Guo Zhijun, Tan Jingyuan, Hou Yanchen
2024,47(6):8-13, DOI:
Abstract:
To address the issue of low accuracy in indoor static target positioning with existing single-antenna ultra-high frequency RFID technology, this paper proposes a new RFID localization method based on an antenna boresight signal propagation model. The method first determines the height position of the target through vertical antenna scanning; secondly, it adjusts the antenna height to match that of the target and then performs stepwise rotational scanning to identify the target′s azimuth angle; furthermore, it utilizes a Sparrow Search Algorithm optimized back propagation neural network to establish a path loss model for ranging purposes; finally, it integrates the height, azimuth angle, and distance data to complete the target positioning. Experimental results show that in indoor environment testing, the proposed method has an average positioning error of 7.2 cm, which meets the positioning requirements for items in general indoor scenarios.
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Zhan Huiqiang, Zhang Qi, Mei Jianing, Sun Xiaoyu, Lin Mu, Yao Shunyu
2024,47(6):123-130, DOI:
Abstract:
Aiming at the force test in low-speed pressurized wind tunnel, the original data source of aerodynamic characteristic curve is analyzed. With the balance signal, flow field state and model attitude as the main objects, combined with the test control process, the abnormal detection methods and strategies of the test data are studied from the dimensions of single point data vector, single test data matrix and multi-test data set in the same period, and an expert system for abnormal data detection is designed and developed based on this core knowledge base. The system inference engine automatically detects online during the test, and realizes the pre-detection and pre-diagnosis of the original data through data identification, rule reasoning, logical reasoning and knowledge iteration. The experimental application results show that the expert system is highly sensitive to the detection of abnormal types such as abnormal bridge pressure, linear segment jump point and zero point detection, which guides the direction of abnormal data analysis and improves the efficiency of problem data investigation.
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Zhang Fubao, Wu Ting, Zhao Chunfeng, Wei Xianliang, Liu Susu
2024,47(6):100-108, DOI:
Abstract:
In real-time detection of saw chain defects based on machine vision, factors like oil contamination and dust impact image brightness and quality, leading to a decrease in the feature extraction capability of the object detection network. In this paper, an automated saw chain defect detection method that combines low-light enhancement and the YOLOv3 algorithm is proposed to ensure the accuracy of saw chain defect detection in complex environments. In the system, the RRDNet network is used to adaptively enhance the brightness of the saw chain image and restore the detailed features in the dark areas of the image. The improved YOLOv3 algorithm is used for defect detection. FPN structure is added with a feature output layer, the a priori bounding box parameters are re-clustered using the K-means clustering algorithm, and the GIoU loss function is introduced to improve the object defect detection accuracy. Experimental results demonstrate that this approach significantly improve image illumination and recover image details. The mAP value of the improved YOLOv3 algorithm is 92.88%, which is a 14% improvement over the original YOLOv3. The overall leakage rate of the system eventually reduces to 3.2%, and the over-detection rate also reduces to 9.1%. The method proposed in this paper enables online detection of saw chain defects in low-light scenarios and exhibits high detection accuracy for various defects.
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Zhang Huimin, Li Feng, Huang Weijia, Peng Shanshan
2024,47(6):86-93, DOI:
Abstract:
A lightweight improved model CAM-YOLOX is designed based on YOLOX to address the issues of false alarms of land targets and missed detections of shore targets encountered in ship target detection in large scene Synthetic Aperture Radar(SAR)images in near-shore scenes. Firstly, embed Coordinate Attention Mechanism in the backbone to enhance ship feature extraction and maintain high detection performance; Secondly, add a shallow branch to the Feature Pyramid Network structure to enhance the ability to extract small target features; Finally, in the feature fusion network, Shuffle unit was used to replace CBS and stacked Bottleneck structures in CSPLayer, achieving model compression. Experiments are carried out on the LS-SSDD-v1.0 remote sensing dataset. The experimental results show that compared with the original algorithm, the improved algorithm in this paper has the precision increased by 5.51%, the recall increased by 3.68%, and the number of model parameters decreased by 16.33% in the near-shore scene ship detection. The proposed algorithm can effectively suppress false alarms on land and reduce the missed detection rate of ships on shore without increasing the number of model parameters.
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Li Ya, Wang Weigang, Zhang Yuan, Liu Ruipeng
2024,47(6):64-70, DOI:
Abstract:
A task offloading strategy based on Vehicle Edge Computing (VEC) is designed to meet the requirements of complex vehicular tasks in terms of latency, energy consumption, and computational performance, while reducing network resource competition and consumption. The goal is to minimize the long-term cost balancing between task processing latency and energy consumption. The task offloading problem in vehicular networks is modeled as a Markov Decision Process (MDP). An improved algorithm, named LN-TD3, is proposed building upon the traditional Twin Delayed Deep Deterministic Policy Gradient (TD3). This improvement incorporates Long Short-Term Memory (LSTM) networks to approximate the policy and value functions. The system state is normalized to accelerate network convergence and enhance training stability. Simulation results demonstrate that LN-TD3 outperforms both fully local computation and fully offloaded computation by more than two times. In terms of convergence speed, LN-TD3 exhibits approximately a 20% improvement compared to DDPG and TD3.
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Chen Haoan, Li Hui, Huang Rui, Fu Pingbo, Zhang Jian
2024,47(6):182-189, DOI:
Abstract:
Facing the challenges of regulating unmanned aerial vehicles (UAV), and based on an YOLOv5-Lite improved model, this paper incorporates an exponential moving sample weight function that dynamically allocates loss function weights to the model during the training iteration. Through model computations, we achieve real-time UAV tracking using a two-degree-of-freedom servo platform. Furthermore, video capture, model calculations, and servo control are all performed locally on a Raspberry Pi 4B.The optimized model maintains the original model's parameter count while achieving a mAP@.5:.95 score of 70.2%, representing a 1.5% improvement over the baseline model. Real-time inference on the Raspberry Pi yields an average speed of 2.1 frames per second (FPS), demonstrating increased processing efficiency. Simultaneously, the Raspberry Pi controls a servo platform via the I2C protocol to track UAV targets, ensuring real-time dynamic monitoring of UAVs. This optimization enhances system reliability and offers superior practical value.
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Zhang Bian, Tian Ruyun, Han Weiru, Peng Yuxin
2024,47(6):109-115, DOI:
Abstract:
In order to solve the problems that the traditional SPD life alarm characterization method can not clearly correspond to the real life state of SPD, and the remaining life model characterized by a single degradation related parameter has poor predictability, a multi-parameter SPD life remote monitoring system based on STM32 is designed. With STM32 as the main controller, the important parameters such as surge current, leakage current, surface temperature and tripping status of SPD are collected in real time, and the status information is uploaded to the One net cloud platform through the BC20 wireless communication module. The One net cloud platform displays and stores the multi-parameter data of SPD in real time, and provides data management and analysis. The SVM classification model is used to judge whether SPD is damaged and the BO-LSTM prediction model is used to predict the remaining life of SPD. Based on the positioning function of BC20, the real-time geographic location of SPD can be viewed on the host computer. The results show that the root mean square error and average absolute error of the BO-LSTM prediction model are 0.001 3 and 0.001 8, and the system can monitor the SPD status in real time, effectively predict the remaining life value of SPD, and give early warning in time.
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Zhou Jianxin, Zhang Lihong, Sun Tenghao
2024,47(6):79-85, DOI:
Abstract:
Aiming at the problems that the standard honey badger algorithm (HBA) is easy to fall into local optimum, low search accuracy and slow convergence speed, a honey badger algorithm based on elite differential mutation (EDVHBA) is proposed. The elite solution searched by the two optimization strategies in the standard HBA is combined with differential mutation to generate a new elite solution. The use of three elite solutions to guide the next iteration of the population can increase the diversity of the algorithm solution and prevent the algorithm from falling into premature convergence. At the same time, the nonlinear density factor is improved and a new position update strategy is introduced to improve the convergence speed and optimization accuracy of the algorithm. In order to verify the performance of the algorithm, simulation experiments are carried out on eight classical test functions. The results show that compared with other swarm intelligence algorithms and improved HBA, EDVHBA can find the optimal value 0 in the unimodal function, and converge to the ideal optimal value in the multimodal function after about 50 iterations, which verifies that EDVHBA has better optimization performance.
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Wang Huiquan, Wei Zhipeng, Ma Xin, Xing Haiying
2024,47(6):14-19, DOI:
Abstract:
To solve the problem of low control accuracy of the tidal volume emergency ventilation for lower air pressure at high altitudes, we propose a dual-loop PID tidal volume control system, which utilizes a pressure-compensated PID controller to adjust fan speed, supplemented by an integral-separate PID controller in order to achieve precise control of airflow velocity.Compared with single-loop PID control, the rapid response and no overshooting are observed in the performance tests of the dual-loop control system at an altitude of 4 370 m and atmospheric pressure of 59 kPa, in addition, the output error of the average airflow velocity decrease to 3.19% (the maximum error is 4.1%), which is superior to that of current clinical equipment. Our work offers an effective solution for high-altitude emergency ventilator tidal volume control, and contributes important insights to the development of ventilation control technology in special environments.
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Fang Xin, Shen Lan, Li Fei, Lyu Fangxing
2024,47(6):20-27, DOI:
Abstract:
The high-frequency measurement data of underground vibration signals can record more specific details about the dynamic response of drilling tools, which is helpful for analyzing and diagnosing abnormal vibrations underground. However, the high-frequency measurement generates a large amount of measurement data, resulting in significant storage pressure for underground vibration measurement equipment. The proposed method uses compressed sensing technology to selectively collect and store sparse underground vibration data and then recover high-frequency measurement results through a signal reconstruction algorithm. In the process of realizing this method, an innovative method of constructing a layered Fourier dictionary against spectrum leakage is proposed, and an improved OMP signal reconstruction algorithm based on layered tracking is researched and realized, which greatly reduces the time required for signal recovery. Simulation and experimental test results demonstrate the method′s effectiveness, achieving a system compression ratio of 18.9 and a reconstruction error of 52.1 dB. The proposed method may greatly reduce the data storage pressure of the measuring equipment in the underground, and provides a new way to obtain high-frequency measurement data of underground vibration.
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Cheng Dongxu, Wang Ruizhen, Zhou Junyang, Zhang Kai, Zhang Pengfei
2024,47(6):137-142, DOI:
Abstract:
For the tobacco industry, there is currently no detection device and method for detecting the heating temperature and temperature uniformity of heated cigarette smoking sets. In order to solve the temperature measurement needs of micro rod-shaped heating sheets in a narrow space, this article developed a cigarette heating rod thermometer, and designed a new structure suitable for temperature measurement of cigarette heating rods. In order to verify the accuracy and reliability of the measurement results of the cigarette heating rod thermometer, uncertainty analysis of the thermometer was performed. The analysis results are based on the "GB/T 13283-2008 Accuracy Level of Detection Instruments and Display Instruments for Industrial Process Measurement and Control" standard. The measurement range is 100 ℃~400 ℃, meeting the requirements of level 0.1. The final experiment verified that the heating temperature field of different cigarettes can be effectively measured.
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Feng Zhibo, Zhu Yanming, Liu Wenzhong, Zhang Junjie, Li Yingchun
2024,47(6):34-40, DOI:
Abstract:
The data bits and spread spectrum codes of the spaceborne spread-spectrum transponder are asynchronous. Due to the influence of transmission system noise and Doppler frequency shift, it can cause attenuation of peak values related to receiving and transmitting spread spectrum codes, leading to a decrease in capture performance. Traditional capture techniques often have problems such as high algorithm complexity, slow capture speed, and difficulty adapting to the requirements of large frequency offsets of hundreds of kilohertz. This article proposes a spread spectrum sequence search method that truncates the spread spectrum sequence into two segments for correlation operations, and combines the signal squared sum FFT loop for a large frequency offset locking, effectively suppressing the attenuation of correlation peaks and improving pseudocode capture performance. MATLAB simulation and FPGA board level testing show that the proposed spread spectrum signal capture scheme can resist Doppler frequency shifts of up to ±300 kHz, with an average capture time of about 95 ms. In addition, the FPGA implementation of this algorithm saves about 47% of LUT, 43% of Register, and more than half of DSP and BRAM resources compared to traditional structures, making it of great application value in resource limited real-time communication systems.
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Peng Duo, Luo Bei, Chen Jiangxu
2024,47(6):50-57, DOI:
Abstract:
Aiming at the non-range-ranging location problem of multi-storey WSN structures, a three-dimensional indoor multi-storey structure location algorithm IAODV-HOP algorithm based on improved Tianying is proposed in the field of large-scale indoor multi-storey structure location for some large commercial supermarkets, hospitals, teaching buildings and so on. Firstly, the nodes are divided into three types of communication radius to refine the number of hops, and the average hop distance of the nodes is modified by using the minimum mean square error and the weight factor. Secondly, the IAO algorithm is used to optimize the coordinates of unknown nodes, and the population is initialized by the best point set strategy, which solves the problem that the quality and diversity of the population are difficult to guarantee due to the random distribution of the initial population in the Tianying algorithm. In addition, the golden sine search strategy is added to the local search to improve the position update mode of the population, and enhance the local search ability of the algorithm. Through simulation experiments, compared with traditional 3D-DV-Hop, PSO-3DDV-Hop, N3-3DDV-Hop and N3-ACO-3DDV-Hop, the normalized average positioning error of the proposed algorithm IAODV-HOP is reduced by 70.33%, 62.67%, 64% and 53.67%, respectively. It has better performance, better stability and higher positioning accuracy.
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Ma Zhewei, Zhou Fuqiang, Wang Shaohong
2024,47(6):94-99, DOI:
Abstract:
A feature point extraction algorithm based on adaptive threshold and an improved quadtree homogenization strategy are proposed to address the issue of low positioning accuracy or low matching logarithms of the SLAM system caused by the ORB-SLAM2 algorithm extracting fewer feature points in dark environments or environments with fewer textures, resulting in system crashes. Firstly, based on the brightness of the image, FAST (Features from Accelerated Seed Test) feature points are extracted using adaptive thresholds. Then, an improved quadtree homogenization strategy is used to eliminate and compensate the feature points of the image, completing feature point selection. The experimental results show that the improved feature point extraction algorithm increases the number of matching pairs by 17.6% and SLAM trajectory accuracy by 49.8% compared to the original algorithm in dark and textured environments, effectively improving the robustness and accuracy of the SLAM system.
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2024,47(6):28-33, DOI:
Abstract:
With the increasing demand for satellite network, vehicle-connected network, industrial network and other service simulation, this paper proposes a multi-session delay damage simulation method based on delay range strategy to build flexible software network damage simulation, aiming at the problems of small number of analog links, low flexibility and high resource occupation of traditional dedicated channel damage instruments. In this method, the delay damage of each session flow is identified and controlled independently, and the multi-queue merging architecture based on time delay strategy is adopted to reduce the resource consumption. The experimental results show that compared with the traditional dedicated device and simulation software NetEm, the proposed method supports the independent delay configuration of million-level links, increases the number of session streams from ten to one million, and reduces the memory consumption by at least 85% under each bandwidth, which meets the requirements of large scale and accuracy, and greatly reduces the system cost.
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Ma Dongyin, Wang Xinping, Li Weidong
2024,47(6):58-63, DOI:
Abstract:
Aiming at the Automatic Train Operation of high-speed train,an algorithm based on BAS-PSO optimized auto disturbance rejection control (ADRC) is used to design speed tracking controller.The ADRC is designed based on the train dynamics model,ITAE is used as the objective function,and the parameters are tuned by BAS-PSO.CRH380A train parameters are selected, The tracking effect of BAS-PSO, PSO and improved shark optimized ADRC algorithm on the target speed curve of the train is compared by MATLAB simulation,The tracking error of the train target speed curve based on the BAS-PSO optimized ADRC algorithm is kept in the range of ±0.4 km/h,which is closer to the target speed curve than the other two algorithms.The results show that the ADRC based on BAS-PSO optimization has the advantages of small tracking error and strong anti-interference ability.
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Shi Shujie, Zhao Fengqiang, Wang Bo, Yang Chenhao, Zhou Shuai
2024,47(6):116-122, DOI:
Abstract:
Rolling bearings play an important role in rotating machinery. If a fault occurs, it can cause equipment shutdown, and in severe cases, endanger the safety of on-site personnel. Therefore, it is necessary to diagnose the fault. In response to the difficulty in extracting fault features of rolling bearings and the low accuracy of traditional classification methods, this paper proposes a fault diagnosis method based on Set Empirical Mode Decomposition (EEMD) energy entropy and Golden Jackal Optimization Algorithm (GJO) optimized Kernel Extreme Learning Machine (KELM), achieving the goal of extracting fault features of rolling bearings and correctly classifying them. Through experimental data validation, this method can extract the fault information features hidden in the original signal of rolling bearings, with a diagnostic accuracy of up to 98.47%.
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Long Biao, Yang Jun, Chen Huiping, Chen Guangrun, Zhao Peiyang
2024,47(6):157-163, DOI:
Abstract:
In order to solve the problem that the audio signal processing in the voice communication system has a large amount of data, a lot of stray signals, and the received audio signals of the frequency modulation receiver are large and small, a lightweight audio signal processing algorithm is proposed, and based on this algorithm, the audio signal receiving and automatic gain control are realized on the field programmable gate array(FPGA) platform. The algorithm combines digital down conversion technology, multistage extraction filtering technology and automatic gain control technology (AGC) technology, and is applied to the audio signal processing system. The RF analog signal received from the upper antenna is converted into baseband audio signal through analog-to-digital conversion and digital down-conversion, and the stray signal in the baseband signal is filtered through four-stage extraction filtering, reducing the complexity and power consumption of the system. At the same time, the digital AGC controls and adjusts the baseband audio signal to output a more stable audio signal. The experimental results show that the algorithm can effectively reduce the information rate from 102.4 MHz to 32 kHz, reduce the computation burden, improve the signal quality, and reduce the resource utilization of FPGA. And the automatic gain control adjustment of audio signal is realized, and the adjustment time is only 12.8 μs, which meets the power stability time of the receiver.
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Zhou Guoliang, Zhang Daohui, Guo Xiaoping
2024,47(6):190-196, DOI:
Abstract:
The gesture recognition method based on surface electromyography and pattern recognition has a broad application prospect in the field of rehabilitation hand. In this paper, a hand gesture recognition method based on surface electromyography (sEMG) is proposed to predict 52 hand movements. In order to solve the problem that surface EMG signals are easily disturbed and improve the classification effect of surface EMG signals, TiCNN-DRSN network is proposed, whose main function is to better identify the noise and reduce the time for filtering the noise. Ti is a TiCNN network, in which convolutional kernel Dropout and minimal batch training are used to introduce training interference to the convolutional neural network and increase the generalization of the model; DRSN is a deep residual shrinkage network, which can effectively eliminate redundant signals in sEMG signals and reduce signal noise interference. TiCNN-DRSN has achieved high anti-noise and adaptive performance without any noise reduction pretreatment. The recognition rate of this model on Ninapro database reaches 97.43% 0.8%.
