Abstract:To address the issues of large size and integration difficulty in traditional Kuband bandpass filters, this paper proposes a novel 8th-order SIW bandpass filter utilizing LTCC technology. By designing a coupling matrix and adjusting the coupling coefficients between the cavities, the filter features an effective coupling topology, with eight SIW cavities interconnected through electromagnetic hybrid coupling, achieving wide bandwidth and high out-of-band rejection. To reduce overall volume, the multilayer plate SIW technique is used, the eight SIW cavities are divided into two layers, with four cavities on each layer,the filter′s volume is significantly reduced. Coplanar waveguide coupling is employed at the microstrip and SIW junctions, introducing two transmission zeros in the out-of-band region to suppress spurious passbands and further enhance out-of-band performance. Experimental results show that the filter has a bandwidth of approximately 13.3~17.2 GHz, a relative bandwidth (FBW) of 26%, an in-band insertion loss of less than -2.2 dB, and out-of-band rejection greater than -20 dB in the 18~20 GHz range.

Abstract:Nanodosimetry simulates the biological effects of radiation by measuring physical quantities, such as ions that are ionized by initial particles. The microchannel ionized ion counting nanodosimeter can measure ionized ions by utilizing an internal electric field to drive them into the microchannel, where they induce an electron avalanche under high voltage. This paper studies the parameter design of a nanodosimeter utilizing microchannel ionized ion counting, based on finite element analysis and the Monte Carlo method. COMSOL finite element analysis and Garfield++ Monte Carlo software are utilized to calculate and simulate the static electric field, the dynamic transport of ionized ions, and the formation of electron avalanches in microchannels. The characteristics of the internal electric field funnel effect were systematically studied under various anode and cathode electric field configurations. Additionally, the impact of these configurations on the dynamic transport and collection efficiency of ionized ions was analyzed. The dependence of electron avalanche on design parameters, such as electric field configuration and microchannel diameter, is examined, and the results are discussed and summarized. The analysis results indicate that selecting an anode voltage of 5~15 V, a cathode voltage of -1 500~-2 000 V, and a microchannel diameter of 0.5~0.75 mm can achieve good measurement performance. The research findings presented in this article will provide a crucial theoretical foundation for a deeper understanding of the internal mechanisms and parameter design optimization of nanodosimeters.

Abstract:To address the issues of slow dynamic response and large current ripple in traditional control of permanent magnet synchronous motors, an improved model predictive control algorithm is proposed based on a novel dual-power sliding mode integral speed controller optimized by the slime mold algorithm. First, the velocity loop adopts a new double power convergence law sliding mode velocity controller to control the motor more accurately. The stability of this method is validated using the Lyapunov function. Second, the slime mold optimization algorithm is applied to optimize the parameters of the dq-axis PI controller, enabling rapid determination of the optimal PI parameters. At the same time, current model predictive control is employed to reduce current ripple. Finally, from a microscopic perspective, a 3D phase diagram of dq-axis current and motor speed (n) is drawn to further verify the effectiveness of the controller. Simulation results show that compared with the traditional PI-MPC, SMC-MPC and NSMC methods, the proposed method NSMC-MPC has significant advantages in dynamic response speed, speed stability and anti-interference ability, which can significantly reduce the overshooting and current pulsation, and improve the dynamic performance and load adaptability.

Abstract:Aiming at the problems of the current OAM antenna, such as fewer modes, and difficulty to conform, this paper designs a terahertz-frequency array antenna using graphene and MXene. By adjusting the feeding phase, different OAM vortex wave modes can be generated. Changing the external voltage, the conductivity of graphene is modified, thus, the operating frequency can be tunable. The impact of bending on antenna performance is also studied, and phase and frequency compensation methods are proposed accordingly. The simulation results show that the antenna can generate numerous OAM vortex waves with integer mode of 0~3 and fractional mode of 0.5、1.5、2.5 by simply adjusting the feeding phase difference. The antenna gain can reach 11.7 dBi, and the operating frequency can be tuned within the range of 1.1~1.9 THz. For significant cylindrical bending, the proposed phase and frequency compensation methods can effectively maintain the vortex wave form and operating frequency. According to the experimental results, the proposed flexible OAM antenna is very promising in applications of antenna deformation and bending scenarios, such as human-machine interfaces, soft robots, and aerospace components, etc.

Abstract:Given the control accuracy problem of the resonant acoustic mixer′s acceleration, a radial basis function neural network (RBFNN) PID optimized by an improved sparrow search algorithm (ISSA) method is proposed to control acceleration. Firstly, the acceleration model is identified through the step response curve. Secondly, the sparrow search algorithm is enhanced by introducing a Tent chaos initializing population and a linear dynamic inertial weight method for updating the discoverer′s position. Thirdly, ISSA is then applied to optimize the parameters of the RBFNN. Finally, the optimized RBFNN-PID is employed for the simulation test of acceleration and compared with other traditional algorithms. The simulation results show that the convergence speed and optimization ability of the developed ISSA are superior to other algorithms. It is found that the RBFNN-PID acceleration control optimized by ISSA can effectively suppress system overshoot and improve system control speed, accuracy, and stability. Experimental results show that, compared with the comparison algorithms, the RBFNN-PID acceleration control system optimized by ISSA demonstrates superior control performance and adaptive capability, providing a great practical value for the acceleration control of the resonant acoustic mixer.

Abstract:Accurate wind power prediction is crucial for ensuring the stable operation of power grids and improving the efficiency of wind resource utilization. To address the non-stationary and intermittent characteristics of wind power data, this paper proposes a combined IZOA-Transformer-BiGRU prediction model based on data decomposition techniques to enhance the accuracy and reliability of short-term wind power forecasting. First, the energy difference method is employed to determine the number of sub-modalities for variational mode decomposition, which decomposes the original wind power with strong random fluctuations into a series of relatively stable sub-sequences, enabling better more effective extraction of temporal features. Next, the Transformer-BiGRU model is constructed, incorporating a multihead attention mechanism to process interactions between multiple features in parallel, while the BiGRU component captures temporal dependencies within the sequence, thus enhancing prediction performance. To further improve the model′s forecasting accuracy, an improved zebra optimization algorithm, integrating singer chaotic mapping, lens refraction-based learning, and the simplex method, is developed to optimize four key hyperparameters of the Transformer-BiGRU model: the number of hidden layer neurons, initial learning rate, regularization coefficient, and the number of attention heads. Finally, the IZOA-Transformer-BiGRU model predicts the subsequences derived from VMD, and the final prediction is reconstructed through aggregation. Experimental results show that, compared to the standalone BiGRU model, the proposed model improves the coefficient of determination by 5.10% and reduces the mean absolute error, root mean square error, and mean absolute percentage error by 56.17%，54.58% and 54.55%, respectively, demonstrating its high prediction accuracy.

Abstract:Aiming at the problem of unstable and random power input in the traditional three-stage charging method of battery, this study proposes a photovoltaic energy storage charging control strategy based on the maximum power point tracking (VSS-POM-MPPT) algorithm of variable step perturbation observation method based on perturbation observation method (POM) and the four-stage charging algorithm. By building a photovoltaic model, the tracking speed of maximum power point tracking (MPPT) of VSS-POM and POM is compared. At the same time, the voltage stabilization accuracy and current stabilization accuracy are used as the performance evaluation indexes of photovoltaic cells for battery charging. The controller program design based on VSS-POM-MPPT algorithm is completed, and the charging experiment of photovoltaic cell to battery is carried out. The experimental results show that the time of VSS-POM-MPPT is 0.008 s less than that of POM-MPPT when tracking the maximum power point, and the speed is increased by 24.3%. The battery charging data recorded in the experiment is consistent with the charging algorithm designed in this study. The voltage stabilization accuracy and current stabilization accuracy are ±0.4% and ±0.8%, respectively, which meet the power industry standards of ±(0.5%~1%) and ±(1%~2%).

Abstract:Due to the diverse height and angle of drone shots, the images often have complex backgrounds and mainly feature small targets. As a result, the performance of detection algorithms for these images is often poor. To address this issue, this paper presents a vehicle detection method for aerial images using an adaptive perception network. The goal is to improve the detection of small targets by focusing on two aspects: enhancing the saliency of vehicle features and improving the preservation of feature information. First, an adaptive perception feature extraction module is proposed to extract a more efficient feature representation. This module captures long-range dependencies and stronger geometric feature representations to adaptively model the shape of objects. Second, a dual-branch spatial perception downsampling module is introduced to mitigate information loss caused by down-sampling and continuous pooling. This module combines feature maps of different channels to maximize the retention of small target feature information. Next, the feature fusion network incorporates shallow feature maps with rich spatial information and adds detection heads to enhance the detection capability of small targets. Finally, a new dynamic regression loss function, DEloU, is designed. This function includes a penalty term to measure the correlation between the aspect ratio of the ground truth box and the detection box, further improving the prediction accuracy of the network. Experimental results on the Visdrone dataset show that the proposed method achieves an average precision (mAP) of 69.9% and an inference speed of 99.26 fps, indicating a good balance between speed and accuracy. Moreover, the proposed method has achieved the best detection accuracy on the UCAS-AOD dataset and has strong generalization ability.

Abstract:In order to deal with the interference caused by false automatic dependent surveillance-broadcast (ADS-B) signals on flight trajectory information, a detection system for ADS-B false targets was designed based on Kalman filtering for the prediction of flight trajectories. The message decoding is based on an ADS-B demodulation system using a software-defined radio platform, with the decoding verification part completed on the Qt end and dynamically displayed using Gaode map. An ADS-B fake message transmission system was created, and the track prediction part was completed based on Kalman filtering. The jump rate detection part was designed based on the positional dispersion of the ADS-B predicted data and the root mean square error. According to experimental tests, for the given fake messages, 90.4% of the jumps were successfully detected. Therefore, this system has a certain capability to detect ADS-B false targets.

Abstract:The evaluation of electromigration lifetime of through-silicon via in 3D packaging often requires multiple equipment and instruments to cope with different test requirements, which not only increases the complexity, but also may introduce uncertainties. The LabVIEW was conducted as the upper computer, the programmable controller and industrial personal computer were used as the core control unit, and the lifetime evaluation system of TSV electromigration was developed by combining the precision power supply, high-precision multimeter, relay array, and sample connecting terminal, so as to ensure the accurate acquisition and monitoring of the key parameters. With the aid of the developed evaluation system for electromigration lifetime of TSV, the characteristic failure properties of TSV were analyzed under different electrical stresses (1×105、5×105 and 1×106 A/cm2) and temperature stresses (25℃、50℃ and 75℃) by carrying out the accelerated electromigration lifetime tests on dual-via TSV samples. The experimental results show that at the same temperature, the higher the current, the faster the TSV samples fail. The failure time of the samples at the current density of 1×105 A/cm2 at 25℃ is about 56.2 h, while it is only 10.5 h at 1×106 A/cm2. When the current is the same, the higher the temperature, the faster the TSV samples fail, and the failure time decreases as the temperature is increased from 25℃ to 75℃ by about 64.9%. The characteristic failure time of the TSV samples is obtained based on their failure time, and the Black lifetime model of TSV electromigration and its parameters are obtained through design algorithms, with Ea=0.672, n=0.665 825, and A=6.089 9×10-130.

Abstract:In the design phase of ARINC661 cockpit display system, a DF file state management method is proposed to address the problems of limited scalability and low storage efficiency faced by DF file state management. Firstly, by refining the attribute structure and response function of form components, a generalized form component model is constructed, which facilitates the unified management of form component states; on this basis, a hierarchical dynamic storage structure is designed, which realizes the optimized storage and fast positioning of target states; in addition, for the backup of the DF file states, lightweight log generation and parsing techniques are investigated, which facilitates the version restoration of DF files. Finally, several typical cockpit display screens are utilized to simulate and test the key technologies, and the results show that the method can quickly and accurately perform DF file state management at the design stage with good scalability and reliability, and the generated state logs take up more than 17.5% less memory space compared with the logs using the traditional format.

Abstract:With the rapid development of autonomous driving, the demand for high-precision real-time vehicle navigation and positioning technology is becoming increasingly urgent. In the commonly used GNSS/INS integrated navigation, adaptive Kalman filtering is a standard state prediction method. However, in complex dynamic environments, it has limitations in dealing with multipath noise from GNSS and real-time variations in process noise. To address this issue, this paper proposes an adaptive anti-noise Kalman filtering algorithm to suppress measurement noise from GNSS and dynamic process noise. The algorithm first preprocesses the original GNSS measurement data using variational mode decomposition and wavelet denoising to improve the input accuracy for data fusion. Secondly, during the data fusion process, a dynamic noise scaling factor that changes in real time with the vehicle environment is introduced. Through these two denoising steps, the overall interference of noise uncertainty on navigation accuracy is effectively suppressed. The effectiveness of the proposed method is verified through simulations and real vehicle experiments. Compared with the traditional adaptive Kalman filtering algorithm, the proposed algorithm reduces the position estimation error and speed estimation error by 37.7% and 42.8%, respectively, significantly enhancing the high-precision estimation capability of vehicle speed and position.

Abstract:This paper proposes a recognition algorithm based on signal-to-noise ratio estimation for the identification of non constant modulus signals in asymmetric paired carrier multiple access (APCMA) in satellite communication signals. Firstly, the algorithm performs power spectrum estimation on the received signal to calculate the observed signal-to-noise ratio; secondly, by calculating the effective signal-to-noise ratio through high-order moment calculation and combining it with constellation moment algorithm, the problem of large errors in the effective signal-to-noise ratio of non constant modulus signals in high-order moment calculation has been solved; finally, based on the characteristic that the observed signal-to-noise ratio of the mixed signal is higher than the effective signal-to-noise ratio, a feature parameter M was designed to achieve the recognition of non constant modulus APCMA signals. The experimental results show that the proposed algorithm achieves a recognition rate of nearly 100% for non constant modulus APCMA signals when the signal-to-noise ratio of weak signals is greater than 0 dB.

Abstract:In view of the problems of low prediction accuracy of the initial moment after the change point of the two-stage residual life prediction model, a remaining useful life prediction algorithm based on the data processing at the change point is proposed. Firstly, the Wiener process was used to construct the degradation model and the expectation maximization algorithm with Bayesian method was used to realize parameter updating. The degraded data were identified at the change point, and part of the degraded data before the change point were determined to be used for the life prediction at the initial moment after the change point to reduce prediction error. Finally, the algorithm was validated using simulation data and NASA test data, respectively. The results show that the prediction accuracy of the proposed algorithm is further improved. According to the prediction results of NASA test data, compared with the single-stage life prediction model and two-stage life prediction model, the root mean square error is reduced by 10.76 and 1.78 respectively, which is of great significance for the prediction of the remaining life of the product.

Abstract:In the field of aeroengine health monitoring, traditional wired sensing systems have problems such as complexity in cabling, poor flexibility, and high maintenance costs. To solve these problems, this paper presents a design and implementation of a self-powered high-frequency vibration signal wireless sensing system targeted for aircraft engines. The system is divided into two main parts: the wireless sensing system and the self-powered system. The wireless sensing system converts vibration signals into analog electrical signals. An ESP32-S2 chip is used as the microcontroller of the wireless sensor node. The embedded Wi-Fi module transmit the signals to a host computer. Finally, the received signals are analyzed and shown on the host computer. Experimental results demonstrate that the system has a sampling rate of 250 ksps, and the system has a transmission amplitude error rate within 3.8% and a frequency error rate within 1.5%. The self-powered device harnesses waste heat from the tailpipe of the aeroengine to generate electricity, consisting of a thermoelectric power generation module and an energy harvesting circuit. The thermoelectric power generation module measures temperatures and adjusts the distance between the thermoelectric modules and the tailpipe to maintain output power at a high level without risk of overheated damage. The energy harvesting circuit employs the BQ25504 chip to harvest the power and store the extra energy in a rechargeable battery. The self-powered device can generate approximately 40 mW of power, which exceeds the system′s power consumption of 26.42 mW, thus enabling self-sustained operation of the whole system.

Abstract:The controller area network (CAN) bus is widely used in industrial data acquisition、internet of vehicles and other fields, making its security intrusion detection very important.To comprehensively enhance the performance of the detection method, a bilinear self-attention mechanism for CAN bus intrusion detection is proposed. Firstly, based on the idea of stacked integration, DNN、CNN and LSTM models are used to extract and generate deep learning layer feature data; then, bilinear layers are used to generate self-attention mechanism and FNet feature data separately, which are then fused with deep learning layer feature data through a residual connection layer, and intrusion detection prediction is performed through a fully connected layer, demonstrating high accuracy, detection rate, and good generalization characteristics.Experiments on the Car_Hacking public dataset show that the accuracy, precision, recall, F1 score and AUC values are 0.951，0.996，0.997，0.960 and 0.984, respectively, and as the number of training epochs increases, the accuracy and loss value error remain within 5% and 10%, respectively, indicating that this method outperforms other comparison methods.Application to IoT experimental devices evaluation shows that this method achieves a detection rate of 99.23% for abnormal attack identification, which has significant promotion value for enhancing the security performance of monitoring and control systems.

Abstract:Current shadow-object instance detection methods rely on fully supervised training with mask labels. However, mask labeling is both complex and costly. Utilizing only bounding box labels for training can reduce annotation challenges and costs, but this weak supervision may decrease the accuracy of instance mask predictions. To address this issue, weakly supervised methods were first utilized for shadow-object instance detection. A weakly supervised shadow-object instance detection approach with bidirectional learning network is proposed. Firstly, a teacher-student bidirectional learning structure was designed, utilizing the predicted results of the teacher network as pseudo mask labels for the supervised training of the student network. The accuracy of weakly supervised detection was enhanced by updating the parameters of the teacher network using the exponential moving average method. Secondly, the prediction mask is precisely positioned using projection loss, and a color affinity index is introduced to represent the color prior information of the image. By integrating this with the cross-entropy loss function, a color affinity loss function is designed to enhance the network′s overall detection performance. To verify the effectiveness of the proposed method and enhance the network′s robustness, a shadow-object instance detection dataset was constructed. The predictive capability of the network was validated using both this dataset and the public dataset SOBA, with average precision values of 53.3 and 51.5, respectively.

Abstract:To addresses the challenge of distinguishing between malignant and benign tumors in breast cancer ultrasound images， an improved method based on the EfficientNet model is proposed. This thesis introduced an enhanced whale optimization algorithm (WOA) and a global context (GC) module to improve the accuracy and efficiency of early breast cancer detection. The model optimizes feature extraction and classification performance by combining depthwise separable convolution and large kernel convolution. Additionally, dynamic hyperparameter tuning and data augmentation were applied to further enhance the model′s generalization ability and stability. Experimental results show that the model achieved an accuracy of 99.81% on the training set and 98.06% on the validation set, significantly surpassing traditional methods. The mean average precision (mAP) was increased from 96.42% to 98.60%, demonstrating the model′s effectiveness in improving the accuracy and reliability of early diagnosis， providing an efficient technical pathway for early screening and diagnosis of breast cancer.

Abstract:Fabric seams detection in industrial setting is becoming increasingly important in textile applications. However, seam detection faces challenges such as small target size, few available features, and complex environmental factors, which make it difficult to ensure stable and real-time detection results. A fabric seam detection algorithm YOLOv8-DVB based on improved YOLOv8 is proposed to address this series of problems. The C2f module is optimized based on the characteristics of Deformable Convolutional Networks v4, a C2f-DCN module with multi-size feature sampling is proposed to strengthen the network′s extraction of feature information of different sizes. In the neck, the BiFPN structure is used as a feature fusion approach, which allows features of different scales to be more fully fused at multiple levels by introducing top-down and bottom-up bidirectional pathways. Additionally, a more efficient VoV-GSCSP module is introduced to lightweight the feature fusion network, which helps the neck network to reduce the computational load and parameter count. Finally, a dedicated small target detection layer is designed to optimize the feature extraction of small targets. YOLOv8-DVB is compared with the original model as well as YOLOv5、YOLOv7 and Faster R-CNN through experiments to verify the detection accuracy and detection precision. The experimental results show that the method obtains 84.7% detection accuracy on the self-constructed dataset, which is higher than the original model and other network models, and is able to quickly and effectively accurately detect the target categories and locations in complex industrial environments.

Abstract:In the application of traffic sign recognition, most of the targets to be detected are small targets, which are prone to problems such as missed detection and false detection. In order to solve these problems, an improved traffic sign recognition algorithm, FKDS-YOLOv8s, was designed based on the YOLOv8s algorithm. FasterBlock is used to reconstruct the C2f module to form a new lightweight module C2f-Faster, which not only improves the feature extraction ability of the model, but also reduces the computational overhead. Based on the SENet and ResNeXt models, a new detection head Detect_SR was designed to enable the model to effectively focus on the key features of small targets. DySample, a lightweight and efficient dynamic upsampler, significantly reduces GPU memory consumption. By increasing the output level of upsampling and prediction, the model can capture rich position information, which effectively solves the problem of insufficient information when the YOLOv8s model processes small targets. The Shape-IoU loss function is introduced to optimize the shortcomings of the original CIoU in the border regression. In addition, the newly designed attention mechanism DKN-Attention is integrated into the Neck part, which locates the attention area of the scene of small objects during the upsampling and downsampling process, and improves the feature extraction and recognition ability of small traffic signs in the distance. The experimental results were carried out on the Chinese traffic sign dataset TT100K, and the results showed that FKDS-YOLOv8s improved the accuracy (P)、recall rate (R) and mAP50 by 5.9%、4.2% and 6.3%, respectively, compared with the benchmark model. Compared with the traditional method, FKDS-YOLOv8s shows significant advantages in performance.

Abstract:In order to solve the problem of limited field of view during ship berthing and achieve the visualization of berthing distance, a berthing distance perception method based on UAV vision is proposed. First, the UAV is used to collect the berthing video of the ship, and the EMA mechanism is added on the basis of the YOLOv8 segmentation model to achieve the fine segmentation of the ship edges. Next, the berth line is extracted by the regional growth algorithm and the Hough line detection. Finally, the closest distance calculation model is used to convert ships and berths into three-dimensional world coordinate system, and the closest distance between ships and berths is searched. The experimental results show that the accuracy of the algorithm after adding the EMA attention mechanism can reach 92.3% of the segmentation accuracy, and the error of the closest distance between the ship and the berth is less than 0.1 m. This method can not only monitor the environment around the berthing ship, but also achieve the visualization of the distance between the ship and the berth, which has a good application prospect in berthing operation.

Abstract:Aiming at the problems of complex image background and poor recognition of small defect targets in the current insulator surface defect recognition, a transmission line insulator surface defect recognition algorithm based on YOLOv8 is proposed. Firstly, the CAF module is introduced in the backbone network to enhance the model′s analysis of complex image scenes and enhance the ability to extract global and local features; secondly, the GD mechanism is added to the neck network of the model to reduce the loss of information in the feature fusion process and improve the small target detection ability; finally, the ATFL classification loss function is used to weaken the interference of complex background on small target detection, and the PIOU bounding box loss function is introduced to improve the recognition accuracy and accelerate the model convergence speed. Experimental results show that the mAP50 of the algorithm reaches 94.1%, the precision rate reaches 92.5%, and the recall rate reaches 91.3%, which are 3.1%、0.7% and 3.9% higher than the baseline model, respectively, and the comprehensive performance is better than the recent YOLOv9s, YOLOv10s and other representative algorithms.

Abstract:To enhance the lightweight human pose estimation network′s ability to extract information and fuse features from different stages of feature maps, as well as improve the post-processing capability of keypoint heatmaps and classification feature maps, a human pose estimation network based on multi-stage and multi-level feature fusion is proposed. First, a multi-level feature fusion module is designed to improve the neural network model′s ability to extract and summarize information from feature maps. Next, a feature fusion branch is designed in conjunction with the feature fusion module to ensure that information from different stages of the model is preserved without being lost due to long convolution operations. Finally, post-processing operations are applied to the model′s output keypoint classification maps, utilizing a classification loss enhancement module for further enhancement, allowing the model to better focus on the keypoint classification task and improve the accuracy of its outputs. Performance testing is conducted on the CrowdPose dataset, where the AP values of the proposed algorithm and the LitePose algorithm under the XS structure are 50.7% and 48.4%, respectively; under the S structure, the AP values are 59.1% and 58.3%. Performance testing is conducted on the MS COCO val2017 dataset, where the AP values of the proposed algorithm and the LitePose algorithm under the XS structure are 41.9% and 40.6%, respectively; under the S structure, the AP values are 57.0% and 56.8%. Experimental results indicate that the multi-scale feature fusion module, high-resolution fusion branch, and post-processing operations proposed in this paper positively contribute to improving the detection performance of the human pose estimation network.