Abstract:In order to reduce the cost and risk of current integrated circuit electromagnetic interference test experiment, an interference generating circuit which can periodically disturb the power supply end of low impedance integrated circuit chip is proposed. The circuit uses low impedance MOS tubes as the output driver, connects multiple MOS tubes to power chips with different output voltage values, and uses FPGA to control the switches of these MOS tubes, which can produce periodic disturbance waveforms with certain frequency and amplitude. The jammer made of the circuit structure can be inserted into the test board in the form of board card and can be powered by ordinary charger. Compared with large equipment such as RF signal source and power amplifier, it has the characteristics of small volume, low power consumption, simple operation, low cost, safety and convenience. The jammer is connected to the load circuit of the chip to be tested with equivalent low impedance for electromagnetic interference test. The test results show that the jammer can generate about 1V voltage disturbance in the range of 0 ~ 30MHz and 0.5V ~ 1V voltage disturbance in the range of 30MHz ~ 80MHz for low impedance load with capacitance of 50pF and resistance of 10 Ω.

Abstract:Aiming at the problems of grid uncertainty disturbance in grid-connected current control of three-phase grid-connected inverter, system coupling in dq coordinate system and the dependence of traditional controller design on accurate mathematical model, a linear active disturbance rejection control strategy based on two-phase stationary coordinate system is proposed. Taking T-type three-level LCL grid-connected inverter as the controlled object, a three-order linear active disturbance rejection controller is designed. Through the equivalent transfer function of the system, the stability and immunity of the inverter system are analyzed in detail, and the effectiveness of the proposed control strategy is verified by simulation and experiment. The results show that the designed control strategy can improve the stability and immunity of T-type three-level LCL grid-connected inverter, and achieve good control of the grid current, and the total harmonic distortion is controlled at 2.2 %. At the same time, the ' bandwidth ' parameter tuning method has clear physical meaning, simple parameter adjustment and high engineering application value.

Abstract:Aiming at the problem that the oil drilling downhole antenna works in a special environment composed of wellbore drilling fluid and strata, the wireless transmission signal loss is large and it is difficult to transmit reliably. Through simulation modeling, a new type of magnetic dipole antenna is designed. The features of interference and miniaturization enable reliable transmission of underground wireless data. Firstly, the HFSS three-dimensional simulation software is used to build a simulation model, and the key parameters such as the length of the antenna, the distance from the drill string body and the number of serpentine bends of the antenna are calculated in detail, and the parameter combination and frequency point law of effective transmission are obtained. According to this rule, the actual antenna is designed, and the simulation results are highly consistent with the actual test results. The return loss can reach below -7.5dB, and the parameters of the simulation design can meet the actual application requirements. The antenna design can be applied to underground wireless transmission in oil drilling, which can improve signal transmission strength, reduce antenna size, reduce system power consumption, and achieve reliable wireless signal transmission.

Abstract:The diamond Nitrogen Vacancy (NV) center has been widely studied as a high-sensitivity magnetometer and has been used in many fields. In order to solve the problems of low fluorescence signal contrast and limited magnetic detection sensitivity, the traditional measurement method only uses a single axial NV centers for excitation and detection, a full - axis excitation method of dual - frequency microwave is used in this paper. Two pairs of resonant peaks are presented in the optical detection magnetic resonance spectrum by the bias magnetic field of a specific [111] crystal direction, and the corresponding dual-frequency microwave is superimposed to achieve synchronous regulation. After experimental verification, the fluorescence contrast of the derived rabbi oscillation signal and spin echo signal is increased by 2.83 times and 3.81 times compared with the uniaxial signal, respectively, and the ultimate magnetic noise sensitivity of the final system is increased by about 14.3 times to 0.76nT/√Hz. Finally, it is proved that full axis excitation is feasible to improve the magnetic sensitivity of the system.

Abstract:Aiming at the multi-user wireless eavesdropping scenario with limited energy in relay node, a security transmission scheme is proposed based on the cooperative non-orthogonal multiple access (NOMA) technology and the simultaneous wireless information and power transfer (SWIPT) technology. In this scheme, the transmit antenna selection mechanism is carried out in the multi-antenna base station. Firstly, the full-duplex cooperative relay technology is utilized for improving transmission reliability and spectrum utilization in NOMA system, and the SWIPT technology is utilized for overcoming the energy shortage problem caused by excessive power consumption of the relay node. Furthermore, the closed-form expressions of outage probability and secrecy outage probability are derived for further measuring the system performance. Finally, the effect of key system parameters on system performance is comprehensively analyzed through asymptotic analysis. Numerical results show that the system performance can be improved by reasonably setting the power allocation coefficient of NOMA user and the power splitting ratio of SWIPT user. Compared with the cooperative NOMA and the traditional NOMA transmission mechanism, the proposed scheme has improved the security performance by 33% and 59%, respectively. Keywords: simultaneous wireless information and power transfer (SWIPT);

Abstract:Aiming at the poor visualization effect of the current seat durability test system and the position state of the test equipment and the stress state of the seat cannot be observed , a seat visualization test system based on LabVIEW has been designed.PCIe-6320 NI DAQ was used to acquire data and pulse output. The system’s host computer monitoring interface was built by LabVIEW and added the equipment’s position monitoring interface and force value visualization interface. The results of the system test showed that it can monitor the posture of the equipment of the test on real time and there was no crash with the system .The limit values of the horizontal and vertical displacement of the equipment are 1100mm and 250mm respectively .And we can be warned in advance if the displacement exceed the maximum and the system would be turned down in time .The force value visualization interface can visually display according to the value of the collected force .The upper and lower thresholds of the curve are established based on the displacement-force value curve collected for the first time and its 22% loss curve .When the displacement-force value curve is within its threshold ,the durability of the seat can meet the requirements

Abstract:In the 5GNR system, the influence of noise cannot be ignored in the channel estimation process at the receiving end, and the traditional LMMSE channel estimation algorithm will have a huge computational load. Therefore, a sliding correlation matrix method based on DMRS is proposed to reduce the complexity of the algorithm. Firstly, the optimal sliding correlation matrix and the number of subcarriers output for each sliding are obtained through simulation, and then output the channel estimation values of frequency domain subcarriers through the sliding window to complete the entire channel estimation; In the time domain, the system performance is further improved by the hybrid interpolation method. Simulation results show that compared with the LMMSE algorithm, the bit error rate of the algorithm is improved by less than 1dB, and the complexity is reduced by 99.74%, which meets the needs of engineering.

Abstract:In this paper, pure angle measurement and location of double theodolite was studied. Considering the non-linearity of measurement equation, unscented Kalman filter (UKF) was used to estimate the system state. Assuming that the target had strong maneuvering, this paper applied the current statistical model (CSM) to fit the target motion state. At the same time, based on the background of non-cooperative target early-warning and tracking, the CSM_UKF tracking model of double theodolite against strongly maneuvering target was established. Under the same conditions, the simulation was compared with the CA model. The simulation results show that the model can track the target stably under the condition of pure angle measurement by double theodolite, especially under the condition of strong maneuvering target, the tracking effect is better than that of the CA model.

Abstract:Aiming at the advantages of UAVs with wider shooting view and more flexible shooting angles and in order to realize and quantitatively evaluate the application of existing object detection algorithms in pedestrian detection and route tracking from aerial view, this paper constructed a pedestrian detection and routes tracking algorithm from aerial shooting view of quad-rotor UAVs. The algorithm adopts YOLOv5 as object detection model, and uses video data collected in practical situation as test data. Model YOLOv5 is trained at first, then through the statistics of the detectin results, the parameters of UAVs’ video-shooting such as horizontal distance, vertical height and pedestrians’ postures during UAVs’ shooting are quantitatively analyzed and verified. Based on the anchor frame, the route curves of pedestrians' movement is outlined to realize path tracking. The results of pedestrian detection and trajectory tracking demonstrate that the algorithm has certain cut-off height of 15 to 20 meters, cut-off angle of arctan3 to arctan4 and cut-off distance of about 20 meters in practical application, but it is less affected by pedestrian postures. Compared with other conventional object detection algorithms, this algorithm has better performance and is expected to be used in situations where the shooting angle is required to be broader and the shooting position is required to be more flexible. In addition, the cut-off height and cut-off distance requirements of the algorithm, calculated by this paper in pedestrian detection quantitatively, has guiding significance for the practical application of the algorithm to carry out UAV detection or rescue.

Abstract:To solve the problem of low population diversity and weak exploitation of sparrow search algorithm, an improved sparrow search algorithm based on orthogonal-opposition-based learning (OOLSSA) is proposed in this paper. First, a normal mutation operator is used to enrich the diversity of algorithm population. Second, the opposition-based learning is used to enhance the ability of the algorithm to jump out of local optimum. Then, orthogonal-opposition-based learning is introduced after the update of the scrounger position to accelerate the convergence of the algorithm. Finally, performance test based on fifteen benchmark test functions, non-parametric Friedman test and balance analysis of algorithms shows that compared with six traditional optimization algorithms and two improved algorithms, OOLSSA has better searching performance on exploration and exploitation ability and convergence speed.

Abstract:A new algorithm is proposed in this work, to solve the disadvantages of UWB(Ultra-wideband) in the complex indoor environment, such as low positioning and navigation accuracy, serious effect of NLOS(Non-line-of-sight) error, and inability to provide target attitude information, which takes position, velocity, quaternion, bias errors of accelerometer and gyroscope as state vectors, fuses UWB and IMU(Inertial measurement unit) measurement information through EKF(Extended Kalman filter) algorithm, corrects velocity, position, quaternion with the bias errors of accelerometer and gyroscope, the quaternion after filtering to calculate the rotation matrix and attitude information. Then, the residual error is used to calculate the measurement noise factor, the residual matrix is composed, and the covariance matrix of the observation noise is dynamically adjusted to suppress the influence of NLOS error on positioning and navigation. The results show that the positioning and navigation algorithm based on the combination of UWB and IMU improves the accuracy of 88.6% compared with the LS-Taylor algorithm in the complex indoor environment, which enhances the system's ability to resist NLOS error, improves the dynamic positioning accuracy, and can get more accurate attitude information, which has better practicability and robustness.

Abstract:Aiming at the problems of slow convergence speed, low search efficiency and poor solution quality of traditional ant colony algorithm in three-dimensional path planning of autonomous underwater vehicle, a three-dimensional path planning method based on improved Empire competition algorithm is proposed. This method takes the traditional imperial competition algorithm as the framework. Firstly, the ant colony idea is introduced into the national initialization part to search the initial path and improve the quality of feasible solutions; Secondly, in order to ensure that the algorithm will not lose excellent individuals while improving population diversity, the idea of differential evolution is added to the part of colonial revolution; Finally, the simulation comparison is carried out on three maps of different scales. The simulation results show that the improved imperial competition algorithm makes full use of the characteristics of fast convergence speed, high convergence accuracy and strong global search ability of the traditional imperial competition algorithm, improves the accuracy of the optimization process, strengthens the global optimization, effectively solves the problem that the traditional imperial competition algorithm is easy to fall into the local optimal solution, and the length of the optimal path is shortened by 11%.

Abstract:Slot allocation is a key technology in TDMA data link network planning. Reasonable time slot allocation can ensure the timeliness of tactical messages and improve the operation efficiency of data link network. Because the traditional slot allocation algorithm can not realize the allocation of any slot; The single intelligent optimization slot allocation algorithm has the problems of low global optimization ability, large amount of computation and low operation efficiency. Based on the minimum uniform slot variance model, a slot allocation algorithm based on genetic tabu search is proposed in this paper. The algorithm makes full use of the advantages of two typical intelligent optimization algorithms: genetic and tabu search, and uses genetic mutation operation to construct diverse neighborhoods, so as to enhance the probability of obtaining the global optimal slot solution; Tabu search algorithm is used to search locally to speed up the convergence speed. The accuracy, stability and efficiency of the proposed algorithm are evaluated by experiments. The results show that compared with the single genetic algorithm and tabu algorithm, the algorithm not only maintains high stability and operation efficiency, but also significantly improves the time slot allocation accuracy. When the number of free slots is 500 and 1000, the accuracy is improved by 6% and 9% respectively compared with the genetic slot allocation algorithm.

Abstract:The flight trajectory is composed of a large amount of time series data and follows certain motion rules. Predicting the flight trajectory of enemy patrol aircraft can effectively improve the survival rate of fighter jets. This paper proposes a flight trajectory prediction method based on C-GRU, aiming at the low accuracy of flight trajectory prediction by the existing single prediction model. Use flight simulation to obtain multiple sets of flight trajectory coordinate point data for C-GRU network model parameter training to achieve flight trajectory prediction. Through the analysis of simulation results, the average absolute error of multiple sets of prediction data on the X, Y, and Z axes of the C-GRU network model is within 4.5m, and the average time overhead of network model prediction is about 4.1ms; Compared with the smaller trajectory data, the two sets of root mean square errors of the Y and Z axes are similar. At the same time, compared with the GRU and LSTM network models, the error is the smallest, and the predicted results are more accurate when the average time-consuming is close. Therefore, the model proposed in this paper is suitable for different flight trajectories, and the prediction results have high reliability.

Abstract:Aiming at the problem that workers are easy to identify and assemble components by mistake under long-term and high-intensity work due to the small volume and similar appearance of components in the assembly process of electronic components, a detection algorithm ETS-Net (Efficient Two-Stage Network) based on deep learning is proposed to realize the rapid and accurate detection of electronic components. The algorithm introduces depthwise separable convolution to reduce the amount of model parameters and computation, and eliminate the complexity of the model. A lightweight and high-performance feature extraction network is proposed to extract discriminative features, K-means clustering and fine-tuning are adopted to obtain a set of anchor boxes suitable for the scene, an efficient regional proposal network is introduced to obtain high-quality proposals. And then, two sibling fully-connected layers are used to predict classes and adjust proposals again, and non-maximum suppression is introduced to reduce redundant detection results. The experimental results show that the algorithm has high robustness and efficiency in the visual detection task of electronic component assembly robot.

Abstract:A crack detection algorithm based on parallel extraction and attention fusion network is designed to address the problems of low accuracy, much noise and detail loss of existing methods for crack detection. First, the high and low-level features of the crack scene are extracted using multi-scale convolutional parallel neural networks with different depths; then, to improve the detection accuracy, the high and low-level features of the crack scene are effectively fused with the pixel attention mechanism for the features of the crack scene to obtain effective fusion features for crack detection; finally, the crack detection results are output using nonlinear mapping. The experimental results show that the proposed algorithm can obtain effective features for high-precision detection results, the details of crack detection results are clearer, and the supervised learning approach largely eliminates the noise interference of detection results and obtains detection results with better visual effects; the proposed algorithm also has good performance in the evaluation of quantitative indexes such as accuracy rate and recall rate, and the accuracy rate of crack detection reaches 85%.

Abstract:In view of the small number of samples in the current wearing mask data set and the limited hardware conditions, this paper proposes a lightweight YOLOv2 mask wearing detection method based on transfer learning. Based on the YOLOv2 target detection method, this method uses the MobileNetV2 of parameter transfer learning as the feature extraction network, which simplifies the network model and improves the training speed. The pre trained MobileNetV2 feature extraction network and YOLOv2 target detection network are combined to form a mask wearing detection network model. This paper collects and establishes a data set of 1000 pictures of face wearing masks to train and test the network model. The experimental results show that compared with YOLOv2 and SSD300 models, the average accuracy of mask wearing detection of MobileNetV2-YOLOv2 model is improved by 3.8%, 2.7%, and the detection speed increased by 2.5 times and 2.4 times. Moreover, under the condition of insufficient light and dense detection, MobileNetV2-YOLOv2 can still effectively detect mask wearing, which has better detection effect and stronger robustness than R-CNN and Faster-RCNN.

Abstract:Aiming at the key problem that the elastic support of the traditional in vitro coagulation dynamic sensor is easy to fatigue, which leads to the decrease of sensor accuracy, a new in-vitro coagulation dynamic sensor based on magnetic levitation was designed. Based on blood coagulation in vitro detection sensor working principle and structural characteristics, the mathematical model of the magnetic space status inside the sensor through the arrangement of different phase angle sensor internal structure for electromagnetic structure simulation, finite element numerical analysis on the relationship between the phase angle and the magnetic induction intensity were analyzed, and the optimization analysis results and set up the experimental test equipment.The device was used to calibrate the parameters of the sensor, verify the design results of the magnetic levitation arrangement structure of the sensor, and compare the data with the imported instrument through matching test with the standard viscosity solution.The results show that when the phase angle of the magnetic levitation sensor designed in this paper is 20°, the internal magnetic induction intensity is 1.46e·10-3wb/m, which is basically consistent with the simulation data. At this time, the amplitude of the sensor is 2.03μm, and the vibration frequency is 150Hz. The repeatability and correlation of the test data were 0.003 and 0.994, respectively. After calculation, the sensor accuracy was 0.002MPa·s. The precision of the dynamic detection sensor of coagulation function designed in this paper can meet the requirements of in vitro coagulation detection, provide core technical guarantee for improving product performance, and play an important role in improving clinical coagulation rapid detection technology.

Abstract:In order to solve the problem that the accuracy of multi-type fault classification of motor bearing is not high, a fault diagnosis method of motor bearing based on the improved Aquila optimization algorithm (IAO) is proposed, which is used to optimize the Support vector machine of motor bearing.Firstly, the basic Aquila optimization algorithm is introduced, and then Tent chaotic map and adaptive weight are introduced to improve the algorithm. Secondly,VMD is performed on the time domain signal samples of rolling bearing faults under 10 states, and the time and frequency domain features of different states are obtained. Finally, the penalty parameter (C) and kernel parameter (g) of support vector machine were optimized by the IAO algorithm, so as to construct the IAO-SVM rolling bearing fault diagnosis model. The final results show that the IAO-SVM model has a high accuracy of 100% in fault diagnosis under 10 states of motor rolling bearings.

Abstract:Aiming at the problem of large background noise and difficulty in extracting effective fault features in the acoustic fault diagnosis of subway wheel bearings, a method for fault feature extraction under strong noise background was proposed. Perform short-time Fourier transform (STFT) on the sound signal to obtain a time-frequency diagram, and the stripes in the time-frequency diagram are the fault features; add the signal intensities of each point of the image along the direction of the stripes to obtain the time-frequency diagram The signal intensity corresponding to the graph is superimposed on a line graph to show the fault characteristics; and an adaptive cyclic noise reduction algorithm based on the peak height is proposed to reduce the noise of the signal intensity superposition line graph, and the evaluation index of the graph is the number of effective peaks; finally An adaptive sliding window detection method is proposed to intercept the fringe distribution area in the time-frequency graph, so as to obtain the optimal fault feature display effect. Experimental results show that the proposed method can extract obvious fault features from the collected audio signals.

Abstract:As an important equipment in the system of oil drilling rig, the bearing capacity of derrick and base directly affects the safety and benefit of oilfield drilling production. Due to corrosion, wear and load increase in rig derricks and bases due to adverse environmental conditions, it is necessary to link the derricks and bases load capacity with safety assessment techniques to ensure the safety of rig production operations. Therefore, this article focus on the base of the rig derrick and safety assessment of carrying capacity and technological research. Firstly, the finite element simulation is used to find out the maximum stress monitoring position under the lifting condition of the derrick and substructure, and then the fiber Bragg grating monitoring technology is used to monitor the strain of the lifting and lowering of the derrick and substructure, the key structural parts of the drilling rig, and the key stress sections under the working condition, so as to prevent the operation beyond the design capacity and accidents. The test results show that the measured strain values of the derrick and substructure of the drilling rig have a linear relationship with the external force load, and the fitting degrees are 99.48% and 99.85% respectively. The system can monitor the strain of the derrick and substructure, realize the pre-warning and post support of the bearing capacity monitoring of drilling equipment, and more safely ensure the exploration and development of oil and gas resources.

Abstract:The test sequence optimization problem is a key problem in the fault diagnosis process; for the test sequence optimization problem of the multi-valued attribute system, the adaptive differential evolution algorithm is used, combined with the characteristics of the multi-valued attribute system, analysed the role of mutation operators in algorithms, and designed the individual coding strategy and two different diagnosis methods, proposed a differential evolution algorithm that integrates Gaussian, Cauchy mutation operators and multi-difference strategies; Through experimental comparison and analysis, the results show that the algorithm can not only be well applied to multi-valued attribute systems, but also when dealing with the test sequence optimization problem of binary attribute systems, compared with the existing algorithms, the number of test points obtained by this algorithm is less. It is expected that the test cost is lower, and it can be used to solve the problem of diagnosis strategy in multi-valued attribute systems.

Abstract:Time-frequency analysis is an important method for extracting bearing fault diagnosis, and it is difficult to extract transient fault features under strong background noise. To address this problem, a bearing fault diagnosis method based on Teager-Kaiser energy operator(TKEO) and synchroextracting transform(SET) is proposed to improve the concentration of the energy of time-frequency analysis of SET. The method firstly extracts the TKEO processing of the collected bearing vibration signal to highlight the impact component of the bearing fault vibration signal; then, the processed signal is subjected to SET analysis, and the time-frequency coefficients of the time-frequency ridges are extracted by the synchroextracting operator(SEO) to realize the extraction of transient fault features; finally, the feasibility of the method is verified by analyzing the simulated signal and the measured signal. The experimental results show that the method can effectively extract the fault characteristics of the bearing, and the analysis results have certain superiority compared with the previous time-frequency analysis methods.

Abstract:Analysis of ship clutter in MMW sea fog detection radar data sea fog detection radar will be interfered by many sources of clutter, and the analysis of these clutter is the basis of improving the sea fog detection effect. In this paper, the ship clutter in the MMW sea fog detection radar data is analyzed, and the characteristics of clutter interference caused by the ship are counted. The separation of ship signal and weather echo is realized, and the data quality of sea fog detection is improved. By further mining the information contained in the clutter, multiple information such as the length, speed and course of the ship is extracted. The potential of weather radar in ship identification is preliminarily explored.

Abstract:The wave measurement system can automatically monitor the wave height, period and direction of the ocean in a fixed area, all weather and all day. A wave measurement system based on STM32F429 processor and MTI-3-8A7G6T inertial sensor is designed. The algorithm and software design of the wave measurement system were completed, in which the wave height and wave period were measured by integrating the acceleration twice in the frequency domain, which could effectively avoid the influence of the DC component on the integral result. Meanwhile, the digital filtering was carried out in the frequency domain to filter out the zero deviation error of the accelerometer and the random noise of low frequency. The measurement of wave direction is obtained from the Angle between horizontal resultant acceleration and magnetic heading. The wave measurement algorithm is successfully transplanted into STM32F429 processor through software design to realize the wave measurement function. Experimental results show that the measurement errors of the wave measurement system are within the specified range for waves with different wave heights and wave periods. The system can solve the wave characteristic value accurately. run stably for a long time. and meet the requirements of high precision and high reliability.