Abstract:Although conventional Virtual synchronous Generator (VSG) solves the problem of lack of inertia of new energy grid-connection and can effectively support the system frequency, it also brings active power and frequency oscillation phenomenon under disturbance. In order to further suppress the power oscillation and improve the dynamic response performance of the system comprehensively, a small signal model of VSG active power control loop was established, and the influence of virtual parameter selection on system performance was analyzed according to the pole distribution diagram of the closed-loop transfer function. Secondly, by observing the curves of power angular and frequency of a synchronous generator, refined fuzzy rules were designed to regulate the value of the virtual inertia. An appropriate damping ratio was selected by simultaneously considering the maximum overshoot of active power, the rate of change of frequency, the settling time and the rise time. The value of the virtual damping was coordinately adapted based on the chosen damping ratio and the relationship between the virtual damping and the virtual inertia. Finally, several VSG control strategies were compared by simulations with Matlab/Simulink to verify the feasibility and effectiveness of the proposed control strategy.

Abstract:When a medical infusion pump is used with a disposable pump infusion set for infusion, the changes in infusion accuracy caused by ambient temperature, infusion time and speed will affect the treatment effect. For this reason, this paper studies the effect of using a pump infusion set on the infusion accuracy with the increase of infusion time under the conditions of different temperatures and medium and high flow rates, a triple logarithmic total model and a logarithmic + hyperbolic total model of infusion time, infusion temperature, and infusion flow rate on infusion accuracy are established. Infusion experiments with flow rates of 100ml/h, 200ml/h, 500ml/h and 1000ml/h are carried out at temperatures of 10°C, 20°C and 30°C, respectively. The univariate effects of time, temperature and flow rate on the infusion accuracy are explored respectively. Finally, the prediction analysis of the univariate model, the parameter identification of the total model and the prediction analysis are carried out. Under the experimental conditions, the root mean square errors of the univariate models are all less than 0.01, and the coefficients of determination are all greater than 0.96. The relative errors between the predicted values of the time and flow velocity models and the actual values are all less than 10%, and the error of the former is even greater, basically less than 1%, the root mean square error of the total model is less than 0.01, the coefficient of determination is greater than 0.93, and the relative error between the predicted value of the total model and the actual value is less than 10%, which verifies the accuracy and effectiveness of the built model. It provides a theoretical basis for calibrating the infusion errors caused by time, temperature and flow rate.

Abstract:Defect detection on inwall of ferromagnetic pipes through low frequency electromagnetic method has become a research hotspot. However, periodic variation of magnetic flux leakage signal brings inconvenience to extraction of defect information. In addition, dependences between magnetic flux leakage signal and the pipe parameters such as length, diameter and initial thickness severely disturb the detection. To solve the problems, in this paper, we firstly establish a two-dimensional finite element detection model of irregular defects on inwall of the ferromagnetic pipes. Subsequently, the impact of periodic variation of magnetic flux leakage signal on extraction of defect information is eliminated by calculating the ratio of magnetic flux leakage signal to coil current. After that, the impact of dependences between magnetic flux leakage signal and the pipe parameters is greatly reduced through preprocessing of Finally, the defect profile is successfully reconstructed using the regression model between defect profile and preprocessed trained by Gaussian process regression algorithm. Based on the above mentioned method, simulation has been performed, and the results indicate that the RMSEs of reconstructed profiles are all around 0.17mm, which verify that the proposed method can accurately reconstruct profiles of the defects on inwall of ferromagnetic pipes.

Abstract:In order to solve the problem of the constant voltage at the armature terminal of the hybrid excitation synchronous generator under different operating conditions, an excitation control system based on the full-bridge converter topology was designed. The excitation system adopts a double closed-loop control strategy with the armature winding voltage as the outer loop and the stator harmonic excitation winding current as the inner loop. Taking the terminal voltage of the harmonic brushless hybrid excitation synchronous generator as the feedback signal, the generator terminal voltage is kept constant by adjusting the current of the stator harmonic excitation winding. The basic structure and working principle of the hybrid excitation generator with double harmonic windings are expounded, the software and hardware of the excitation control system are designed, and the performance of the excitation control system is tested. The experimental results show that the terminal voltage of the generator can be well adjusted by adjusting the current of the stator harmonic excitation winding, and the output voltage of the generator can be kept constant under different loads through the automatic adjustment function of the excitation control system.

Abstract:Transcutaneous auricular vagus nerve stimulation (taVNS) is an emerging treatment method for psychiatric and cardiovascular diseases, and its stimulation intensity setting needs to adjust the stimulation current to the pain threshold and then reduce its amplitude. This approach not only lacks uniformity, but it also has an impact on treatment efficacy and comfort. To estimate taVNS pain thresholds, this research provides a novel technique that combines HRV characteristics and machine learning regression models. Based on the experimentally collected data, the prediction accuracy of HRV characteristics as input to various machine learning models was systematically compared. The results show that the combination of HRV characteristics and extra trees regression has the best performance, and the use of genetic algorithm to remove redundant features can effectively improve the model prediction performance. The root-mean-square error ranges from 1.18 to 1.56, while the mean-square error ranges from 0.77 to 0.96. This method can be utilized to predict taVNS stimulation intensity and has a positive effect on the treatment effect of subjects during taVNS.

Abstract:In order to improve the positioning accuracy of the unmanned distribution vehicle, the GPS/BDS and IMU multi-sensor fusion technology is applied to the positioning system of the unmanned distribution vehicle. To solve the problems of low positioning accuracy and poor anti-interference which caused by signal loss and cumulative error in GPS / BDS and IMU positioning solution. In this paper, CKF algorithm is used to filter the positioning results from GPS/BDS and sins. That it will improve the positioning accuracy. When the GPS/BDS positioning receiving module signal is missing, combine IMU which provides data set and SINS algorithm to get the current position of the unmanned distribution vehicle; In dealing with the cumulative error in IMU positioning process, CKF is used to correct the GPS/BDS receiver data. In order to verify the superiority of the positioning solution method integrating GPS/BDS and IMU, a single BDS positioning system is used in the experiment to compare the positioning results. The results show that the method used in this paper reduces the speed error by 27.89% and the position error by 38.81%, which can effectively improve the positioning accuracy and stability of the unmanned distribution vehicle in the process of delivering goods.

Abstract:In order to monitor the heat transfer efficiency of the direct air-cooled radiator，the method of modeling is used to study and predict the heat transfer performance of the air-cooled radiator. The outlet temperature of the air-cooled radiator can indirectly reflect its heat transfer capacity，which can be used as an evaluation index of the heat transfer performance of the air-cooled radiator. According to the analysis of the radiator heat transfer model and the influencing factors of heat transfer performance，the Radial Basis Function (RBF) neural network model was established with environmental wind speed，environmental temperature，fan speed，exhaust steam pressure，exhaust steam temperature and unit load as the input and outlet temperature as the output. In order to avoid the model falling into local optimum，Particle Swarm Optimization (PSO) algorithm was used to optimize the parameters of RBF neural network，and a large number of air cooling tower operation data were used to train the RBF neural network，and then simulation verification was carried out. Experimental results show that the MAE and RMSE of the optimized model are the lowest, and the comparison with RBF and PSO-BP models verifies the superiority of the proposed algorithm in temperature prediction.

Abstract:Aiming at the characteristics that the traditional Prony algorithm is easily interfered by noise and there is correlation between multiple power quality signals in the same area, in this paper, the Prony harmonic detection algorithm based on multi-channel signal joint denoising is proposed to achieve the accurate detection of harmonics under strong noise conditions. Firstly, the central frequency method and trajectory similarity method are used to improve the multivariate variational mode decomposition algorithm.Then, the improved MVMD algorithm is used to jointly decompose the associated multi-channel signals, extract the dominant mode components, and reorganize them into stable signals suitable for Prony harmonic analysis. Finally, Prony analysis is performed on the stable signals to obtain preliminary harmonic parameters, and the threshold screening and artificial fish swarm global optimization are carried out to obtain the accurate harmonic detection parameters. Simulation experiments show that the output signal-noise ratio of the improved MVMD denoising algorithm is 37.3, which is higher than VMD denoising method (33.2) and wavelet denoising method (32.8),and the denoising effect is better; The error of the harmonic detection result of the algorithm in this paper is generally less than that of the traditional Prony algorithm. It possesses the characteristics of high harmonic detection accuracy and simultaneous calculation of multi-channel signals.

Abstract:In order to improve the accuracy and efficiency of point cloud registration when there are large view changes, a point cloud registration method based on affine-invariant feature cloud purification and improved stochastic gradient descent is proposed in this paper. Firstly, the 2D feature matches with the ability to resist the change of view changes are obtained, and the point cloud purification method is designed to estimate the initial pose transformation of the point cloud based on the spatial topological relationship of the feature cloud. Then, on the basis of stochastic gradient descent method, a fast clustering nearest neighbor search strategy is designed to enhance the efficiency of searching for the corresponding points. The learning rate of the stochastic gradient descent is dynamically adjusted in probability to improve the global convergence. The experimental results show that the proposed point cloud registration method has a good adaptability to large viewing changes, and can effectively improve the accuracy and efficiency of registration.

Abstract:In this paper, for a class of nonlinear systems, a robust augmented extended Kalman filter is proposed based on descriptor systems combined with the improve whale optimization algorithm to search the system noise optimal solution, so that the accurate estimation of concurrent actuator and sensor faults for the nonlinear system is implemented. Firstly, the concurrent faults are regarded as the state variable of the nonlinear system, a descriptor systems is established, and the fault estimation of the nonlinear system is transformed into the state estimation of the nonlinear descriptor systems. Then, a robust upper bound is proposed to decrease the influence of linearization error on estimation accuracy. Furthermore, the noise is optimized by the improve whale optimization algorithm to optimize the robust augmented extended Kalman filter. Finally, the longitudinal motion simulation model of F-16 aircraft is given, the algorithm designed in this paper is used to compare with adaptive unscented Kalman filter and robust augmented extended Kalman filter based on whale optimization algorithm. The simulation results show that compared with the other two algorithms, the root mean square error of fault estimation of the algorithm designed in this paper is reduced by about 50%, which verifies its superiority.

Abstract:Pedestrian tracking is a hot topic in deep learning research. The current tracking algorithm has the problems that it cannot meet the real-time performance and frequent ID conversion due to the high similarity of the tracking targets, the occlusion between the targets, and the irregular motion. In order to improve the running speed, a lightweight network combining CNN and transformer is used in the target detection stage, and a joint detection method is adopted to share feature weights, calculate detection, re-identification, and human pose estimation branches in parallel, and adjust the number of convolution channels of each branch at the same time. . The tracking part uses the target motion information predicted by Kalman filtering, the target re-identification information, and the position information of each key point of the target pose to complete the target identity matching, which reduces the frequent conversion of the same ID. The experimental part uses the MOT16 dataset for training and testing. The multi-target tracking accuracy (MOTA) of this algorithm is 48.5%, the multi-target tracking accuracy (MOTP) is 78.17%, the FPS is 20, and the model size is 18.4M. Experiments show that the proposed tracking algorithm improves the overall tracking performance, and the real-time performance and accuracy meet the expected requirements.

Abstract:The fine-grained image recognition algorithm based on the ViT model has some problems, such as feature extraction is not comprehensive and parameter selection is not universal. To solve these problems, this paper presents an Adaptive Feature Extraction method with Indirect Attention (AFEIA). Firstly, to classify the characteristics of the object as most relevant, less relevant, and irrelevant, the improved natural breakpoint classification algorithm is used. This method can extract the most discriminative features adaptively for different input samples, which ensures the accuracy of feature extraction. Secondly, the attention weight matrix is used to obtain the features that are indirectly related to the object. This method acquires subtle differences between objects and ensures comprehensive feature extraction. Experiments show that the ViT model using the AFEIA method achieved 91.6% and 91.5% prediction accuracy on two fine-grained datasets CUB-200-2011, and Stanford Dogs, respectively. Visualization methods and ablation experiments verified the effectiveness of the AFEIA method.

Abstract:Due to the great difference of imaging methods between optical and SAR images, it is very difficult to extract a unified set of optical and SAR objects. Therefore, this paper proposes a geographic object-oriented integrated segmentation method of high resolution optics and SAR. This method is different from the traditional integrated segmentation strategy of processing heterogeneous images at the same time, but only segmenting optical images, so as to obtain a reliable set of geographical objects; On this basis, the marker points are extracted adaptively for each object, and projected into the SAR image according to the coarse registration results; Finally, the region growth based on marker points is carried out in SAR image, and finally the object set matching the segmented object of optical image is obtained. The experimental results of several groups of optical and SAR images show that the optical SAR matching object set extracted by the proposed method is closer to the actual geographical object, and the j-value can reach more than 7.8, which is significantly better than the comparison method in visual analysis and quantitative evaluation.

Abstract:In order to solve the problems of small target, intensive, poor accuracy, slow detection speed and difficult application in helmet wearing detection task, this paper proposed a EfficientNetV2-SSD algorithm based on SSD network. Aiming at the problem of multiple SSD network parameters, the improved lightweight network EfficientnetV2 is used to replace the feature extraction network in the SSD to reduce network parameters and improve detection speed. For small targets that are difficult to detect, top-down and bottom-up FPN pyramid structures are used to enrich the information of all prediction feature layers to the maximum extent and improve the detection accuracy of small targets. Aiming at the characteristics of the detected target such as helmet, the size and proportion of the prior frame are redesigned to improve the accuracy of small target detection, accelerate the convergence speed of the network and reduce the network volume. The experimental results show that EfficientNetV2-SSD improved the average accuracy of helmet wearing detection by 7.01% and the network volume was reduced by 75% compared with the SSD network, with better practicability.

Abstract:An object-face based RGB-D image stitching optimization method is studied to solve the problem of low resolution, small range, and high noise of RGB-D depth images, which is not conducive to three-dimensional reconstruction. First, the RGB-D images are pre-processed and aligned; the feature points are extracted and roughly matched using the algorithm. Then, the mismatching is eliminated by the corresponding relationship of the same object face under different perspectives studied in this paper. Finally, the RGB-D images with wide viewing angles and three-dimensional models are obtained based on the homography matrix. Three algorithms, Scale-Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), and Oriented FAST and Rotated BRIEF (ORB), are used for comparison experiments. The experimental results show that 41%, 29%, and 52% erroneous matches are removed on distorted and revolved images, and the Root Mean Square Error is reduced by 5%, 27%, and 33% respectively. In the scaled image, 53%, 57%, and 51% erroneous matches are removed, and the Root Mean Square Error is reduced by 14%, 17%, and 28%, which improves the matching accuracy and verifies the feasibility of this method.

Abstract:Due to the large intra-class gap of remote sensing scene images, that is, the feature information of the same category of images is quite different, the accuracy of classification based only on feature information is not high, and the existing remote sensing scene image classification methods ignore the same common information of the same category. It can assist in image recognition. This paper proposes a remote sensing scene image classification method based on the fusion of common and characteristic information. First, the simple feature map and the complex feature map obtained by the shallower and deeper layers of the convolutional network are superimposed on the image, which can be considered as the feature map with concentrated attention of the image, and the handcrafted feature LBP of this feature map is extracted as the common information. It is then fused with the feature information extracted by the convolutional network and classified. In this paper, the SVM classifier whose hyperparameters are optimized by Bayesian optimization is used to achieve the best performance to eliminate the influence of the classifier on the experiment. Experiments on the two datasets UC Merced and AID verify that the classification accuracy reaches 98.80% and 96.06%, respectively, indicating that the method can effectively improve the accuracy of remote sensing scene images. It is of great significance in the fields of national defense, urban planning, and geological exploration.

Abstract:In order to solve the problem of dynamic detection of both sides of round holes on rails, a dynamic measurement method of round holes based on machine vision was proposed. Firstly, the image of the round hole is dynamically collected by an industrial camera and preprocessed. The radius of the outer circle and the inner circle of the image corresponds to the dimensions of both sides of the round hole. Secondly, the center and radius of outer circle are obtained by edge detection and gradient method, and the influence of irrelevant edges is reduced by mnemonic search during edge detection. Then, the contour points of the inner circle were obtained by searching the outer circle region in the polar coordinate system, and the inner circle size was finally obtained by iterative fitting, and the dynamic measurement of the dimensions of the two sides of the hole was realized. The experimental results show that this method has high precision, and the measurement errors of both sides of the circular hole are less than 1 pixel when the illumination is 2000 lx.

Abstract:Overhead transmission line robot can replace artificial power inspection tasks, solve the low efficiency in manual inspection Labor intensity The problem such as intelligent degree is insufficient Put forward between your checking design train of thought, through the design of mechanical structure optimization identification algorithm, realize the independent walking of the robot Wireless charging image acquisition intelligent inspection In the aspect of software, a special program and interface are developed based on C#, and a data set based on overhead transmission line hardware and defect is established. On the basis of YOLOv4 target detection algorithm, through data enhancement The boundary box optimization and model structure improvement, the establishment of YOLOV4-M model, optimize the performance of power hardware identification and detection, improve the environmental adaptability of the system application through the robot online operation and testing, the identification algorithm in the speed of 45fps, the average detection accuracy of 97.6%.

Abstract:The motor imagery brain-computer interface has been widely used in the field of brain-computer interconnection due to its greater autonomy and flexibility. Compared with other paradigms, the classification accuracy rate is low, which limits its development. In this paper, two feature analysis methods of time-frequency atlas and brain topography were used to analyze the EEG signals of upper limb motor imagery, and the filter bank co-space (FBCSP) feature extraction algorithm was used to analyze the characteristics of upper limb motor imagery. The signal data is extracted with features, and then the extraction results are divided into three types: support vector machine (SVM) algorithm, K-Nearest Neighbor (K-Nearest Neighbor) algorithm, and back propagation (BP) neural network. According to the classification algorithm, the research results found that the average classification accuracy of the SVM algorithm, KNN algorithm and BP neural network algorithm applied to the upper limb motor imagery brain-computer interface system was 76.45%, 74.55%, and 81.70%, respectively. Compared with the SVM, the BP neural network algorithm The algorithm and KNN algorithm are 5.25% and 7.15% higher in the classification accuracy of the upper limb motor imagery task respectively, and the classification accuracy obtained after the t test has a very significant statistical difference, and the ROC curve and AUC value were used to detect Compared with the SVM algorithm and the KNN algorithm, the AUC value of the BP neural network is also increased by 0.1226 and 0.1285 respectively, indicating that the BP neural network classification algorithm is more suitable for the upper limb motor imagery brain-computer interface system than the SVM algorithm and the KNN algorithm. , improve the classification accuracy of the system, and promote the development process of the practical application of upper limb motor imagery EEG signals.

Abstract:In order to solve the nonlinear ill-conditioned problem of capacitance and permittivity in electrical cpacitance tomography (ECT) image reconstruction, An adaptive weighted multi-feature fusion (AWMF)ECT image reconstruction algorithm is proposed to realize the nonlinear mapping between capacitance value and dielectric constant is fitted by network model. Firstly, dense convolutional network (Densenet) is used in the network model, which not only alleviates the phenomenon of gradient disappearance, but also integrates the characteristic information of different channels. The weights of the feature channels are adjusted adaptively by squeeze excitation network (SENet) to extract the key features of the different channels to improve the accuracy of the image reconstruction. Secondly, the tree aggregation structure network (TASN) Network module is constructed to expand the receptive field and extract rich multi-scale characteristic information to eliminate artifacts brought by ordinary convolution. After modeling and simulation on COMSOL5.3, the image was reconstructed by MATLAB2014a. Experimental results show that the reconstructed image error coefficient is reduced to 0.0256, and the correlation coefficient is up to 0.9747. Compared with the traditional algorithm and CNN algorithm, the reconstructed image has higher quality.

Abstract:The structural characteristics and flight principle of the small coaxial folding twin-rotor aircraft are analyzed. The kinematics and dynamics of the UAV are studied and a mathematical model is established. A test platform for dynamic performance and attitude angle of UAV semi-physical flight is established. The platform analyzes the performance of aircraft mechanical vibration, attitude angle and noise. Through the test experiments, the characteristics of mechanical vibration and noise generated by different rotor speeds of the UAV are analyzed. And the influence on the attitude calculation results of MEMS sensors such as gyroscopes and accelerometers. Test experiments show that the vibration amplitude increases with the increase of rotational speed. The vibration characteristics provide a new idea and useful reference for the establishment and evaluation of the noise model for the robustness and anti-interference of the UAV.

Abstract:Aiming at using autoencoder to extract process features for fault detection, the local structure information of data is not considered, a method of neighborhood denoising quadrature autoencoder is proposed. The neighborhood preservation embedding algorithm extracts the neighborhood information of the data as a weight to weight the process data and strengthen the local structure information of the data. The orthogonal autoencoder further extracts the nonlinear features of the process data with local information weighting. The robustness of the autoencoder is enhanced by adding noise, and the network parameters are trained by the back-propagation algorithm to obtain a robust autoencoder model that can capture the local and global characteristics of the data. T2 and SPE statistics are constructed in the latent feature and reconstructed residual space of the model, respectively, and the statistical control limits are calculated for fault detection. Simulation experiments are carried out on the Tennessee-Eastman process and the three-phase flow process, and the results show the effectiveness of the proposed algorithm.

Abstract:Aiming at discontinuous object edge segmentation in high-resolution remote sensing image semantic segmentation and low accuracy of small object segmentation, this paper proposes a remote sensing image segmentation algorithm based on improved DeeplabV3+. The algorithm first adopts distraction network called ResNeSt instead of the DeeplabV3+ original backbone network Xeception to extract richer deep semantic information, thereby improving the accuracy of image segmentation; secondly, the Coordinate Attention (CA) mechanism is introduced to effectively obtain more accurate target location information of segmentation to make the segmentation target edge more continuous; finally, the cascade feature fusion method (CFF) is adopted in the decoding layer to improve the semantic information representation ability of the network. The experimental results show that the algorithm has a high mIoU of 97.07% on the high-definition remote sensing image dataset of a city in southern China, which is 3.39% higher than that of the original model and a reflection of better utilization of image semantic feature information. This provides a new way of thinking for remote sensing image semantic information.

Abstract:Aiming at the problem of hydrogen embrittlement caused by diffusion and accumulation of hydrogen in materials, a characterization method based on eddy current signal is proposed. Taking the electrochemical hydrogen charging sample as the research object, firstly, the hydrogen distribution of the sample in the electrochemical hydrogen charging process is calculated based on Fick's law. Then, based on the principle of electromagnetic induction, the eddy current detection finite element model of material hydrogen embrittlement under different hydrogen distributions is established by using COMSOL multiphysics. The distribution of induced eddy current field of the sample excited by time-harmonic electromagnetic field and the electromagnetic field diagram of the detection coil are analyzed by numerical calculation. The in-situ tensile test quantitatively characterized the degree of hydrogen embrittlement. The correlation mechanism between hydrogen embrittlement index and eddy current response signal was analyzed for experimental verification. The results show that the degree of hydrogen embrittlement of metal materials is closely related to the hydrogen content, and the eddy current signal under the action of a time-harmonic electromagnetic field is linearly related to the degree of hydrogen embrittlement, which verifies the effectiveness of the finite element model for eddy current evaluation of material hydrogen embrittlement.

Abstract:CCTV inspection technology is widely used in underground drainage pipe defect detection, but the defect imagescollected by CCTV need to rely on professional inspectors for inspection and identification, and the results are subjective and time-consuming. In order to automate underground drainage pipe defect detection and identification, an improved YOLOX-based underground drainage pipe defect identification method is proposed. Firstly, for the problem of too small data set, the original image is preprocessed by StyleGAN2 to generate multi-defect images. Second, to improve the detection accuracy, the feature fusion layer of YOLOX is improved by borrowing the idea of convolutional pooling pyramid in the null space and introducing the SE attention mechanism to solve the problem that the top layer features contain only single-scale features and are not fused with other feature maps, and a weight-based feature fusion module is designed to solve the feature blending problem brought by the fusion of different feature layers. Finally, the YOLOX boundary loss function is changed to CIOU to improve the efficiency of target detection frame regression. The experimental results show that the proposedalgorithm can well identify five defects, namely, deposition, leakage, tree root invasion, cracks and misalignment, with an mAP of 68.76%, which is 1.62% better than the original YOLOX algorithm.

Abstract:In order to further demonstrate the practicability and accuracy of the near-field to far-field transformation algorithm of equivalent magnetic current method in plane near-field measurement. According to the method of moments (MOM), the radiated electric field equation of the equivalent magnetic current on the aperture surface of the planar antenna is established. The waveguide probe is used to collect the electric field distribution of the planar near-field of the antenna to be measured, and then the conjugate gradient method is used to solve the matrix equation to obtain the equivalent magnetic current distribution on the aperture surface of the antenna, and then the far-field pattern of the antenna is calculated with Green's function. Finally, two types of horn antennas are tested. At the same time, the far-field pattern calculated by the equivalent magnetic current method and the far-field pattern calculated by the traditional plane wave expansion method are compared with the measurement results of the compact field antenna test chamber. The results show that the far-field pattern inversed by this algorithm is more consistent with the compact field measurement results than that inversed by the plane wave expansion method under the same test data. For the pyramidal horn antenna, the radiation pattern shows perfect agreement over of for both E-plane and H-plane. For double ridged pyramidal horn antenna, the radiation pattern has excellent agreement up to for E-plane, and for H-plane, which shows acceptable results for the test. The accuracy of the planar near-field measurement using equivalent magnetic current method is verified.

Abstract:Aiming at the high peak to average power ratio of the filter bank multi-carrier system with offset quadrature amplitude modulation, the DFT-spreading technology is improved and applied to FBMC/OQAM system, a technology called Pruned-DFTs which by removing the input base pulse to spread DFT matrix has been proposed. By removing the base pulse in the DFT matrix, symbols on fewer subcarriers at the transmitter are mapped to more subcarriers for transmission, which reduces the overlap between subcarriers to decrease the PAPR of the FBMC/OQAM system and makes the system have better performance in terms of peak to average power ratio.This technique fully exploits the single carrier effect of DFT-spreading like Single Carrier - Frequency-Division Multiple Access (SC-FDMA) and solves the problem of poor peak to average power ratio in multi-carrier system . Finally, the simulation results show that this scheme can achieve the same peak to average ratio performance as SC-FDMA and effectively reduce the Bit Error Rate (BER) of the system while reducing PAPR. Moreover, this technique can greatly reduce the time overlap of signals transmitted by the system, and make the average transmitted power show almost perfect rectangular shape, but the computational complexity is only slightly increased by 2 times.

Abstract:To solve the problems of low accuracy and long identification time of insulators in transmission lines, An improved method for identification of YOLOv5 insulators is proposed. Firstly, the quality of image samples in the dataset is improved by introducing super-resolution convolutional network. Secondly, by introducing k3-Ghost structure to replace common convolution in BCSP module of original network, the number of parameters in main network of model is reduced, the SE attention module is introduced in the tail of the trunk network to strengthen the model's attention to channel information and improve the performance of target detection; In the neck network, DC-BiFPN structure was introduced to replace the original structure, and different weights were assigned to different scale features to make better fusion of multi-scale features, so as to improve the insulator recognition effect. Finally, CIOU is used as regression loss function to speed up network convergence. The experimental results show that the proposed method has a higher recognition speed while ensuring the accuracy of insulator recognition, with detection accuracy up to 89.5% and detection speed up to 35.7FPS, which verifies the effectiveness of the improved method.