Abstract:In this paper,a multi-disciplinary and multi-domain collaborative design method of large-scale aperiodic phased array antenna is expounded,according to its characteristics. Then,the collaborative design about two antennas is carried out, focused on the typical process of collaborative design and several key control points. In the testing stage, the main indexes of the antenna are measured, the flatness of the radiating end face of antenna 1 is ≤1.2mm, the grating lobe level is ≤-20dB, the flatness of the radiating end face of antenna 2 is ≤1mm, and the grating lobe level is ≤-18dB. The measured data are in good agreement with the simulation results, and all the indexes are better than the design input requirements, which proves the effectiveness of the collaborative design method. Finally,the research direction in the future about large aperiodic array antenna is designated.

Abstract:The problem of stabilization for robust H-∞ for a class of nonlinear uncertain networked control systems with network dual-channel induced time delay and packet dropout is studied in this project. The influence of network induced time delay and data packet dropout on the system is transformed into an equivalent delay which obeys the Bernoulli second interval distribution, while considering the uncertainty of the system as well as external interference, a new nonlinear system is established based on the T-S fuzzy system. The model, through the construction of Lyapunov-Krasovkii functional and linear matrix inequality methods, gives sufficient conditions for the existence of a state feedback controller that makes the nonlinear system robust progressively stabilized and satisfies the performance index of H-∞. In the end, numerical simulation results are used to verify the feasibility and effectiveness of the theoretical analysis method.

Abstract:Aiming at the problem of large filtering error of laser data when actually measuring topography, a topographic survey and processing method based on laser electric cloud data is designed. Collecting laser point cloud data, combining public target method and least square method to complete the point cloud data and automatically filter non-ground data, transforming the plane coordinates and elevation of laser point cloud data, generating topography contour lines of the area to be measured, and realizing topography measurement of the area. The performance of the design method is verified by experiments. The results show that the method can realize the splicing and filtering processing of laser point cloud data at scattered stations, and the filtering performance is stable and the filtering error is low, with an average of 2.1%. It can accurately measure the topography of the experimental area, with an average coordinate error of 0.187, which meets the needs of high-precision measurement of topography in practical applications.

Abstract:With the development of aviation electromechanical system, the detection and life prediction of battery become one of the important work of ground crew. In view of the difficulty of lithium battery life prediction under the condition of small sample, the advantages and disadvantages of GM (1:1) model, grey Verhulst model and neural network model in solving the problem are studied. Through the analysis of existing models, a grey Verhulst neural network model is proposed, which makes up for the defect of low prediction accuracy in the medium and long term of grey model, and reduces the influence of neural network on the sample size requirement. Taking the lithium battery of a certain type of aviation equipment as an example.The results show that the prediction accuracy of grey Verhulst neural network model is 0.7%, which is far lower than that of other models. It shows that the model has high accuracy, which proves the feasibility and effectiveness of the proposed method.

Abstract:High-voltage power lines are often wrapped in plastics and kites which need to be removed in time, however the existing manual removal methods exist potential safety hazard. Taking the advantage of small divergence angle, good directivity and high brightness of laser, foreign matter can be effectively and contactless cleaned up. The laser damage test system has been built to measure the damage threshold of typical foreign matters. According to the results of the experiment, a fixed focal length beam emission system has been designed, which consists of three separate spherical mirrors with simple and compact structure. The length of the system is 272 millimeters, and the weight is 48 grams. The working distance of the system can be adjusted by changing the length between the laser source and lens. The system reaches the diffraction limit on the image quality. Base on the design, the system can remove foreign matters at distances from 20 meters to 200 meters through optimizing the distance from the source to the lens. Simulation result shows that a fundamental Gaussian beam emitted by the system at difference distance has a target diameter less than 9 millimeters, ensuring the effectiveness of the system.

Abstract:Aiming at the problem that large-scale electric vehicle access has adverse effects on the economic operation of microgrids, an orderly charging and discharging control method for electric vehicles based on peak-valley time-of-use electricity prices is proposed. The transmission closure method is used to divide the peak and valley electricity price periods, and taking into account the user's responsiveness, the impact of different charging and discharging behaviors of car owners on the load of the microgrid is analyzed, and an electric vehicle that increases the revenue of the microgrid under the premise of ensuring the stability of the microgrid The mathematical model of optimal scheduling for orderly charging and discharging, and the improved gray wolf algorithm using chaos theory to solve the problem. Through simulation, it is proved that the proposed strategy can reasonably allocate the charging period of electric vehicles, encourage users to participate in the electricity price response, increase the revenue of the microgrid, and realize the coordinated operation of electric vehicles and microgrid.

Abstract:Be aimed at the complex of the system structure and the circuit composition, and the low linearity and sensitivity of LVDT used in industrial testing, design a hollow LVDT, use the DC constant current as its excitation signal, and the ARM microcontroller controls the switch bridge to charge or discharge the original coil. During the stable discharge period, the microcontroller control logic is used to capture the pulse height difference between the two secondary coils to measure the displacement. The final test data show that the full scale of the hollow LVDT designed by this method is 3mm. The relative percentage accuracy is 0.3%, the sensitivity is 0.284 words / µm, and the high frequency response characteristic,which can meet the general industrial automatic control and measurement requirements.

Abstract:Traditional ultrasound Doppler fetal monitor uses analog demodulation to analyze fetal heart rate. Because the demodulated signal in this mode is a single channel signal, vector analysis can not be carried out. Therefore, in the practical application process, there is repeated recognition of positive and negative movement of mitral valve, resulting in the phenomenon of heart rate doubling. In this research, we designed and implemented a wireless ultrasound Doppler fetal monitoring system based on digital demodulation mode. The system uses the time domain windowing method to demodulate the ultrasonic echo signal, and analyzes the heart rate according to the vector velocity curve. The system can effectively avoid the phenomenon of heart rate doubling. At the same time, the system uses the data wireless transmission scheme between the host and probes, which greatly facilitates the use of operators. According to the results of standard fetal heart phantom test, the system can effectively solve the defect of heart rate doubling existing in traditional equipment, and realize stable, reliable and convenient fetal monitoring.

Abstract:Based on the development and application of UAV flight control computer technology, this paper designs a flight control computer hardware platform for small UAVs based on Zynq platform. The hardware platform adopts SOC processing architecture, realizes hard real-time processing of hardware peripherals through dual ARM hard cores and peripheral FPGA integrated on a single chip, which greatly reduces the software burden. This paper describes in detail The power supply design, air pressure altitude, airspeed design, interface design of the hardware platform. Various test results show that the UAV flight control hardware platform of this architecture has higher integration, smaller size, and strong expansion capability. Each design unit can achieve the expected goals and is suitable for harsh working environments. It has good versatility for fixed-wing aircraft.

Abstract:The current RF dynamic switching system of the vector signal generator proposed is too long and the power loss is high. In order to solve the above problems, the vector signal generator RF dynamic switching system is designed based on time division multiplexing (TDM) technology. The AV1443 vector signal generator is used as the vector signal generator. During the design process, the application program sends the chip to convert the vector signal Information is processed in a unified manner, and data output and input ports with information conversion are designed. The dynamic calculation of the radio frequency state is performed in a multi-channel environment. The discrete thinner algorithm is used to express and analyze the radio frequency dynamics of the vector signal. The experimental results show that the TDM-based vector signal generator RF dynamic switching system can shorten the switching time and reduce the power loss.

Abstract:Aiming at improving network stability and energy efficiency, an incomplete clustered routing algorithm was proposed to solve problems that fast energy consumption and easy death of nodes and unreasonable cluster-head election and short network stable period in cluster routing protocol of LEACH. Considering remaining energy of nodes in cluster-head election, the proposed algorithm takes advantage of sufficient energy of base station to establish incomplete cluster. With combination of flat routing and cluster routing to transmit data, the algorithm can fairly distribute energy consumption in the entire network. Simulation results show that the networks performance better in terms of stable period by 29.48% and the proportion of stable period in lifetime to 72.53%.

Abstract:In order to solve the contact profile scanner due to defective components are installed and the problem that the deviation caused by the result of the measurement data, this paper analyzes the main source of error, based on the idea of least square method, the minimum residual sum of squares as the optimization target processing error of the system model is established, and establish a set of based on LM algorithm iteration the field calibration method of system parameter, and using Matlab software programming algorithm is verified the feasibility and robustness of algorithm in experiments, simulation experiments data shows that the algorithm can realize accurate calibration of system parameters and coordinates of correction, fast convergence rate, High accuracy can control the measurement error below 0.05mm, Angle error control below 0.1°, greatly reducing the scanner's manufacturing and installation accuracy requirements.

Abstract:Targeting at improving the accuracy of multi-classification problems of epileptic EEG signals, one algorithm based on the combination of a signal to difference module and convolutional module is proposed. The signal to difference module performs multi-order differential operations on raw EEG signals to obtain its incremental representation which depicts the fluctuation features of EEG signals. Then, this representation is converted to images by convolutional module using dynamic learning parameters rather than static transformation. And pre-trained convolutional networks are applied to extract features and classify them automatically. The classification results show that this method improves the classification performance by up to 8.1% when compared to recent researches. This method achieved 99.8% accuracy in two-class classification problems, 92.8% accuracy in three-class classification problems and 86.7% accuracy in five-class classification problems, which indicates that signal to difference module has an important effect on EEG classification problem.

Abstract:On the basis of the requirements of civil aviation authorities, combining with the stages and milestone nodes of developing the aircraft, working contents of compliance demonstration in different phases are determined, and a method of collaborative planning of compliance demonstration and type certification is put forward. Combining with the different states of aircraft design, manufacture and test, a method to compile the operational compliance demonstration plan that is in harmony with the development and certification processes is proposed. A multi-parameter scheduling model of operational compliance verification is built based on projects, resources, cost and time. A genetic algorithm (GA) based solving flow of the operational compliance verification scheduling model is designed. A case shows that the method is feasible in dealing with the optimal problem of verification cost and time for multi-objective constraints.

Abstract:The electrical anisotropy of the earth is universal, and the theoretical research and practical application of electromagnetic method are mostly based on the assumption of isotropic theory. Therefore, it is very necessary to study the response characteristics of electromagnetic field based on anisotropic theory. Starting from Maxwell's equation, this paper derives the partial differential equations of the magnetotelluric field under two-dimensional tilt anisotropy, and uses the finite element method to perform numerical calculations on a series of designed two-dimensional anisotropy models. The results show that whether changing the anisotropy coefficient or changing the dip angle can cause significant changes in the apparent resistivity and phase curves. And due to the influence of dip angle, the apparent resistivity curves are asymmetric in space. The results provide theoretical basis and technical guidance for the processing and interpretation of magnetotelluric data.

Abstract:Aiming at the problems of low control accuracy and low reliability of the traditional material mixing device, this paper proposes to use Siemens S7-1200 PLC and touch screen to control and monitor the working process of the material mixing system, so as to realize the control functions of the two materials from feeding, stirring to discharging. Control unit based on TIA Botu fully integrated automation software platform to compile ladder diagram. Touch screen as a function of PC to realize man-machine interface interaction, the test shows that using modular programming software instead of traditional relay control, the control valve to realize opening and closing function accurately, liquid level fluctuation simulation process is true, accurate, the system runs stably, not only can greatly improve the production efficiency, and can reduce the failure rate.

Abstract:Flooding disaster prediction is an important part of emergency management; How to get disaster data in real time and accurately and carry out correlation analysis is a difficult point in disaster investigation. This paper is committed to the research on the perception and fusion of multi-source data of flood and geological disasters. Through the fusion of multi-source data, data analysis engine and algorithm model, data assimilation technology, core professional model and artificial intelligence analysis and other key technologies, a lightweight big data platform is built. The platform includes a flood prediction model and a small visual disaster information system, which can flexibly present the monitoring and analysis results. The data analysis system and algorithm model of this platform form virtual monitoring data through numerical calculation. Entropy method, hot spot technology and machine learning method are used to build up statistical models for water level, flow rate and other data. And the multi-source data could be the parallel verification method, which is helpful to greatly reduce the monitoring points and operation and maintenance costs. This platform can also connect the disaster data association analysis platform with the manual command and decision-making system, and provide the basis for prediction, decision-making and command.

Abstract:Automated 3-D Breast Ultrasound (ABUS) equipment has limited imaging field due to the limitation of probe size. By stitching image data of different fields of view, a panoramic ultrasound inspection of breast tissue is revising. The algorithm first uses the algorithm to locate the nipple position to identify and mark the nipple position in the ABUS image; secondly, the nipple position is designation as the feature matching point of the subsequent stitching algorithm, and the image is integrated the matching feature points to complete the ABUS breast ultrasound panorama. Automatic splicing of pictures. The results show that this algorithm can effectively stitch the ABUS breast ultrasound panoramic images without manual intervention. Doctors can use the panoramic images to diagnose breast cancer screening cases more accurately and objectively, which has very important clinical application value.

Abstract:The designed multi-video interface ultraviolet imaging system supports the output of image data to the PC for display through the USB3.0 interface, and creatively proposes to output the image data to the Hi3519 processor for display through the MIPI CSI-2 interface, and proposes two Type of interface conversion method. Designed the image acquisition module of the imaging system, the image processing module constructed by the field programmable gate array (FPGA) and the hardware description language (Verilog), the I2C communication module and the interface bridge conversion module, among which the image processing module is the main control of the system The core module controls the image acquisition module to complete the acquisition of image information, controls the I2C communication module and the interface bridge module to complete the real-time display of the development platform, and the system integrates bilateral filtering algorithms for smooth noise processing, and designs image mode switching , High Dynamic Range (HDR) mode switching, image zoom and image flip and other modes. Through engineering testing and verification of the system, the system can stably provide a video stream with a resolution of 2048*2040@44fps. The results show that the system has the advantage of low power consumption, and finally completes the precise matching of ultraviolet imaging system and applications in professional fields.

Abstract:Liquid medicine is easy to mix with insoluble foreign matter during the production process. Therefore, the liquid medicine must be tested in time before being put on the market. A detection method based on machine vision and convolutional neural network is designed for ampoule liquid detection, which is different from the traditional sequential image detection algorithm. First, Canny edge detection is used to extract the edge of the ampoule bottle, and the image of the liquid area is cropped,Reduced the amount of subsequent calculations; secondly, the VGG16 convolutional neural network is used for feature extraction of insoluble foreign bodies, which can extract abstract features other than traditional features. Finally, through transfer learning and fine-tuning, 378 of the 400 test samples were identified as correct.The results show that this method can detect insoluble foreign bodies and meet the actual production requirements.

Abstract:In this paper, we propose a geometric active contour approach driven by implicit flows for segmenting deformative objects. The geometric active contours capture dynamical shapes by yielding initial level-set to image features. However, the interesting objects are often associated with salient motions, which has been ignored by naive level-set methods and thus intrinsically limit the harmonizing range. According to hydrodynamics, the implicit flows involving advections and diffusions are formulated to directly guide the level-set updating. To generate the advective vector field, a regression algorithm with smoothing and sparse is embedded in terms of finite motion vectors among sequence. Also the implicit flows are simultaneously synthesized with the non-uniform diffusions restrained by spatial gradients. The proposed improved geometric active contour model based on advection vector field and diffusion flows is a unified and efficient framework, also the topological preservation is inherited from the original geometric active contours. Experimental results of applying this method to real scenes show that the method has fast convergence speed and can accurately segment deformable objects with global motion.

Abstract:Pointing to the problem of obvious step like pattern appearing in transition regions between neighboring windows during the dynamic image generating by conductivity data acquired by electric imaging logging in stratum, image blending method based on Laplace pyramid is designed in this paper. Based on the gray-level images generating up and down step and reverse extending, OpenMP architecture is adopted to execute the blending of extended images up and down on multi-steps of whole well section in parallel. Experimental result is that steps in images are removed effectively, dynamic images are generated with more natural transition and computing speed is improved by about 4 times compared with serial computing on electrical imaging logging data of four wells with different geological types. Results show that compared with two-dimensional image fading method based on mesh morphing, the method is simpler with higher efficiency and more greater application value which ensures post interpretation and processing and provides better service ability for exploration and development of oilfields.

Abstract:In order to solve the problems of high algorithm complexity, long operation time and large amount of data calculation in the real-time image processing process of the gray level co-occurrence matrix for remote sensing cloud image feature extraction, a hardware acceleration method of Vivado HLS to realize the satellite remote sensing cloud image feature extraction algorithm is proposed. This paper studies the gray-level co-occurrence matrix texture feature extraction algorithm and Vivado HLS hardware acceleration design, uses Vivado HLS to hardware-accelerate the gray-level co-occurrence matrix texture special diagnosis extraction, and encapsulates it as a callable IP core to remotely sense cloud images on the PC side The processing results are compared with the processing results after Zynq7020 hardware acceleration. The experimental results show that this method can quickly resolve the texture characteristics of remote sensing cloud images, speed up the processing speed of remote sensing cloud images, and overcome the disadvantage of FPGA design of image algorithms.

Abstract:In order to solve the long endurance cumulative error and real-time problem of inertial navigation system, a multi-source information fusion algorithm based on factor graph is proposed. Based on sum product algorithm, the factor graph model is used to estimate the state based on the maximum a posteriori probability, the mean and variance of variables are calculated to complete data fusion, and the UWB technology and vision sensor are used to calibrate the inertial navigation system with accumulated error to complete error correction. The simulation results show that the error of each axis is 0.36% and 0.31% respectively, and the solution time is 50ms. The error of this algorithm is only one third of the cumulative error of inertial navigation, and its real-time performance is better than other algorithms.

Abstract:In order to prolong network lifetime and improve the reliability, a constructive interference based low-power and reliable data collection protocol (CI-DC) for wireless sensor network is proposed. In CI-DC protocol, data gathering is achieved in a way that sensor nodes initiate flooding in a circular manner by using time division multiple access (TDMA) technology. In order to tackle this dilemma of time synchronization for TDMA, implicit time synchronization algorithm is improved to ensure the time alignment of sensor nodes carrying out constructive interference based flooding. Extensive simulations and testbed experiment results show that it achieves almost 100 % data collection. Compared with traditional classical data collection protocol CTP, CI-DC provides 2.2 times of energy-efficient collection while achieving end-to-end collection reliability. Evaluation has verified that the proposed is able to greatly improve network performance in terms of lifetime and reliability.

Abstract:In order to improve the recognition rate of the frost and snow recognition system, a frost and snow recognition system powered by edge AI was designed. The design comprised the character of Hi3559A both excellent computation and low power consumption. The hardware used reduce power and resource consumption by Module reuse. For the software, by integrating the characteristics of the high computing power of Hisi, embedded processors Hi3559A and IMX334, and starting from model training, model quantization and model deployment, the author improved the MobileNetV2 image classification network and ISP adaptive processing algorithm. The final recognition rate reached 99.7%, pre-processing time reached 0.3s, image classification time reached 0.8s, module average power was 2W, and has its properties being robustness and stable. Hence，the Hi3559A intelligent phase machine module can effectively correct the inaccuracy of the data from the snow depth detector.

Abstract:In order to solve the problems of low data transmission rate of large-capacity storage devices, long-distance transmission stability, low portability of existing Gigabit Ethernet hard-core MAC controllers, poor maneuverability, and waste of resources under specific structure functions, FPGA and Physical layer chip combination method. Through the understanding of the structure and working principle of the MAC layer, a method of combining FPGA and physical layer chip is proposed to complete high-speed data transmission. Due to the FPGA implementation, the MAC controller has the characteristics of strong controllability, strong portability, small resource usage and strong adaptability. It is verified on the basis of a self-developed platform: it shows that the MAC controller is feasible and controllable.

Abstract:Signal-to-noise ratio is an important standard to realize adaptive adjustment of parameters in adaptive configuration of LoRa networks. In order to increase the accuracy and stability of SNR estimation for LoRa modulation, an improved SNR estimation algorithm for LoRa modulation is proposed in this paper. On the basis of the SNR estimation algorithm based on spectral analysis and the characteristics of LoRa modulation, the SNR estimation parameter r is defined, and the empirical formula between r and SNR at different SF is determined through experiments, so as to accurately estimate the SNR. The simulation results show that in the best case the SNR that can be accurately estimated by the improved algorithm is improved by -5dB, and when the root-mean-square error of the improved algorithm can reach 2dB the SNR is improved by -14dB. Therefore, compared with the SNR estimation algorithm based on spectral analysis, the improved algorithm has higher accuracy and stability.

Abstract:Brain-computer interface is a transformative human-computer interaction. Brain-computer interfaces based on EEG account for most of the research, and functional near-infrared spectroscopy based brain-computer interfaces are beginning to be valued by researchers because of their unique advantages. In the study, fNIRS was used to measure the oxygenated hemoglobin (HbO) concentration of 15 subjects during walking imagery and idle state, and to perform band-pass filtering and baseline drift correction of HbO signals. Then we extracted the mean, peak, root-mean-square and their combinations of HbO as classification features, and finally used SVM, KNN and LDA for classification, and tested the classification performance of different time windows during the task. The experimental results show that the classification accuracy of the three combined features extracted by SVM is significantly higher than other features and classifiers, reaching 90.374.42%; the classification accuracy of the 2~8s time window is higher than that of other time windows. This study is expected to provide a new alternative active rehabilitation training method for patients with walking dysfunction.

Abstract:The electrical clearance and creepage distance of electrical products is an important test in electrical safety testing, aiming at the problems that the testing personnel do not understand the standard in place, can not find the shortest path and the measurement results are not accurate. Based on the technical analysis of specific examples, this paper introduces the methods and skills of bridging, plane folding, making the shortest straight line or vertical connection, reasonably selecting the optimal path of electrical clearance and creepage distance, and then using the combination of direct measurement method and calculation method to accurately calculate the measurement results. The results show that this method is very accurate and efficient for solving the two difficulties in the test of electrical clearance and creepage distance: selecting the optimal path and accurately calculating the measured value, which has strong practicability and reference significance.

Abstract:A fiber optic temperature sensor based on the multimode interference effect in the hollow core fiber(HCF) is proposed. which is a three section structure of single mode fiber - hollow core fiber - single mode fiber (SMF-HCF-SMF) ,A section of the HCF with an inner diamete of 5um and a length of 20mm. A broadband light source is used to illuminate the sensor structure , monitor the transmission spectrum of the sensor, and use the sensitivity of the sensor's multimode interference characteristic peak wavelength to temperature to realize temperature monitoring. The temperature rise experiment was conducted in the range of 30-80℃, and the temperature sensing characteristic with good linearity and sensitivity of 24.3pm/℃ was obtained. The proposed sensor has advantages of compact structure, simple manufacture, high reliability.

Abstract:Aiming at the problems of low efficiency of manual verification methods and high development cost of reading recognition system when the measurement verification laboratory verifies meters, a Raspberry Pi-based automatic measurement verification laboratory meter reading recognition system was designed. The system uses the Raspberry Pi 4B to control the camera to collect the picture of the meter reading, and then uses the SSDA algorithm to locate the image reading area, and perform character segmentation, and then use the BP neural network and feature matching algorithm to recognize the segmented characters, and finally use the SSH method to recognize the results Send to PC for storage. The experimental results show that the running time of the system is 500-600ms, and the accuracy of reading recognition is above 92%, which proves that the system can be used for automatic recognition of meter readings in metrological verification laboratories.

Abstract:Aiming at the problem that the wireless signal of surface acoustic wave sensor is easily interfered by the same frequency signal in the environment, an anti-interference algorithm based on ICA blind source separation theory is designed. This algorithm separates the the mixed signal of the sensor the same frequency interference, and then distinguishes the signal based on the attenuation and constant amplitude characteristics of the separated signal waveform. MATLAB simulation results show that the algorithm can effectively separate mixed signals and retain the time-domain waveform characteristics and frequency-domain information of the source signal. The anti-interference algorithm is implemented on a DSP-based signal acquisition and processing platform on which signal separation experiments are carried out. Different sensor signals and interference signal amplitudes are set and the separation and discrimination results are recorded. The experimental results show that when the signal strength of the source signal is relatively close (The signal-to-interference ratio is in the range of 0.8-1.4) and the noise effect is negligible, an accurate discrimination rate of more than 95% of the sensor signal can be achieved, effectively suppressing the same frequency interference.