Abstract:TACAN System is near range military radio navigation system, Aim at the problem of Jump 、invalid and accuracy error of airborne TACAN ranging, the cause of problem is analysed, the operating principle of TACAN ranging is introduced. Fall together the integrated communication navigation and identification system, a plan of double antennas diversity receiving design and implementation for the integrated airborne TACAN ranging is raised. In the plan, the constitution of integrated airborne TACAN is introduced, the working process of double antennas diversity receiving is designed, the data selection strategy of double antennas diversity receiving is analysed, the strategy of decision-level data selection based on measurement results is raised and implemented, the optimum estimation with Kalman Filter is used in strategy, the optimum results is output by comparing prediction residual of the distance, solve limitations and problem of the handoff of antennas of airborne TACAN ranging, and it has been successfully utilized in engineering practice.

Abstract:In view of the current situation that the Moistening Weather Phenomena can only be observed by man, an automatic monitoring method is designed. In the system of temperature, humidity sensors and online calibration data monitor based on STM32, Kelvin Four-terminal sensing and differential self-calibration are adopted. Air temperature, relative humidity and surface temperature of floor can be measured accurately. And dew-point temperature is calculated by inverse function of New-Magnus formula. Then "Moistening Weather" process can be monitored in real time. Experiments show that the measurement can prompt the exact time of generation and disappearance. Light warning is sent when trend angle A≤15°, moderate warning is sent when 15°＜A≤30° , and severe warning is sent when A＞30°. It is important reference for waterproofing measures. The design can provide reference for the development of particular weather phenomenon monitoring.

Abstract:The band pass filter is an important component of the rendezvous parameter millimeter wave detector, and its performance directly affects the detection ability of the detection signal.Its performance directly affects the detection ability of the detection signal. According to the design requirements of millimeter wave detectors, a design scheme of bandpass filter based on loaded stub T-type resonator is proposed. On the basis of calculating the resonant frequency of the resonator and the structural size of the filter,According to the dual-mode stepped impedance resonator model, a microstrip bandpass filter with a center frequency of 36GHz and a bandwidth of 1GHz is obtained through branch loading, combined with HFSS software modeling and simulation optimization.The simulation results show that the band interpolation loss of the millimeter wave narrowband filter is less than 3dB, the in-band return loss is greater than 20dB, and the stopband attenuation is greater than or equal to 20@35.5GHz and greater than or equal to 20@36.5GHz,Basically meet the requirements for the use of millimeter wave detectors.

Abstract:In order to get more accurate motion state parameters of vehicle driving simulator, the 7-DOF loop dynamic model and calculation model of vehicle are established firstly, and the second-order nonlinear ordinary differential equations with tire rolling angular velocity and steering angle of driving wheel as inputs and transverse longitudinal and yaw angle as outputs are obtained. Then, the second-order nonlinear ordinary differential equations are reduced. Finally, the fixed step length is set, and the parameters of vehicle motion state are calculated in turn. The simulation results show that the proposed method has higher accuracy, the maximum displacement error is only 0.0037m, the maximum speed error is only 0.02m/s, the maximum yaw rate and yaw angle error are only 0.0057 rad/s and 0.0047 rad respectively, and the computation time is reduced by 43.2% compared with the extended Kalman filter, which can greatly improve the fidelity of simulation driving.

Abstract:In view of the difficulty in collecting and sorting the parameter information of coal mine drilling equipment, combined with the requirements of drilling technology of drilling equipment, the functional framework, E-R model and database structure of drilling equipment parameter database are constructed. At the same time, with real-time optimization of drilling parameters, lithology identification and real-time browsing as the key contents, the multi-objective parameter optimization model, application of DMP monitoring, data mining and so on are designed The drilling parameter database system has the functions of multi tree data storage and data block adjustment. The experimental test and analysis show that the database system based on C / S structure works normally, and the performance improvement effect is obvious in the operation decision of drilling equipment and the identification of coal mine rock mass characteristics, which helps to improve the efficiency of drilling construction management and the intelligent level of drilling equipment.

Abstract:In present terahertz spectrum measurement, the commercial lock-in amplifier for signal detection has disadvantages such as function redundancy, bulky size and high price. A simplified lock-in amplifier circuit was designed in view of these disadvantages. The circuit uses a STM32 controller based on ARM Cortex-M3 generating chopper signal and reference signal with programmable relative phase, driving a balanced modulator/demodulator to realize lock-in amplification. A test system for current mode lock-in amplifiers was proposed based on the Norton’s theorem to evaluate the performance of the designed lock-in amplifier. Test results show that the maximum relative error of designed lock-in amplifier is only 0.56%, and the dynamic range reaches up to 84.2dB, which means that the designed circuit meets the requirement in THz-TDS system. The size of the designed lock-in amplifier circuit is only 85×85 mm2. Through a RS232 port, the reference signal phase, system gain and time constant can be programmed and the data acquisition can also be completed, which is convenient for integration and application for THz-TDS system .

Abstract:Aiming at the problem that the interference beam of EW phased array can't aim at the target due to the error of the measurement result of the warning receiver, this paper proposes a wide beam-based EW phased array operation mode, designs the array direction pattern that can cover the interference space domain by using genetic algorithm, and analyzes the jamming efficiency of the wide beam. The results show that the electronic warfare phased array based on wide beam can still work normally and improve the reliability of the equipment under the abnormal conditions such as large direction finding error, false alarm and type identification error of the warning receiver. In addition, to solve the problem of widening the burn through distance caused by the decrease of the transmission gain of wide beam, some suggestions are put forward to improve the power of the array channel and switch the working mode according to the distance.

Abstract:The deep sea prompt gamma ray spectrometer is a high-energy gamma ray detector which is planned to be carried by deep sea in-situ Autonomous Underwater Vehicle. The spectrometer is designed to prospect for metallic minerals using Prompt Gamma Neutron Activation Analysis. When operating at sea floor, gamma ray intensity is attenuated by sea water and it's inconvenient to supply power. As a result, a readout system with high efficiency and low power is required. This study aims to design a readout system which is able to operate in deep sea. BGO (Bismuth Germanium Oxide) crystals is used as scintillator of the detector due to its high density. And Silicon Photomultipliers (SiPMs) are used as photosensor for their small size and high gain. A readout system , including Front-End Module, Signal Conditioning Module, Data Acquisition Module and General Interface Module is designed . The system is tested with radioactive source and energy resolution of the spectrometer is 9.9%@1275keV. The readout system for deep sea prompt gamma ray spectrometer is able to operate according to instructions and the resolution meet the requirement of the project.

Abstract:In order to solve the problem of "signal reflection" in the communication circuit, this paper designs a reflectionless notch filter to "absorb" the microwave signal at a specific frequency point , and improved the stopband performance and absorption efficiency of the existing reflectionless low-pass filter by cascating reflectionless notch filter. Using the GaAs IPD process to design the layout, the layout size of the devices is only 800um*500um*87.71um, 1500um*500um*87.71um. The layout simulation results show that the reflectionless notch filter can effectively absorb microwave signals in specific frequency, the average absorption efficiency of more than 90%. The improved reflectionless low-pass filter has a good stopband performance, and the insertion loss of less than 2.5dB in the DC-3.5GHz frequency band, the insertion loss of 20dB at 1.4 times the cutoff frequency and the insertion loss of 40dB at 1.7 times the cutoff frequency. In terms of stopband signal absorption, within the frequency range of DC-7.5GHz, the return loss of the filter is always greater than 15dB, and the absorption efficiency of the reflected signal is more than 95%.

Abstract:Aiming at the mutual restriction of life loss and economic cost of ship energy storage system, this paper proposes a method of ship energy storage system configuration optimization based on multi-objective genetic algorithm. A multi-objective optimization model with life loss and economic cost as the objective function is established. A multi-objective genetic algorithm is used to find a set of Pareto optimal solution sets, and the optimal compromise solution is selected from the Pareto optimal solution set through the TOPSIS algorithm. Finally, using the MATLAB platform's powerful simulation function and simple language features, the dual-fuel generator set electric propulsion ship micro-grid system is used as the research object, and a simulation example is designed. The results show that in the application of the ship energy storage system, after the optimization of the algorithm, the economic cost increased by 0.97%, but the life loss was reduced by 8.7%, which can effectively reduce the life loss while controlling the economic cost almost unchanged , Taking into account the service life and economic benefits of the ship's energy storage system. It is verified that the optimal configuration method in this paper is effective and has important reference value for the optimization of energy storage system configuration in actual production applications.

Abstract:Most of the current pollutant concentration prediction methods do not take into account the temporal and spatial characteristics of meteorological data, which results in a great loss of forecasting accuracy. To this end, this paper presents a spatio-temporal hybrid prediction method (GPR-EN), which can extract both temporal and spatial characteristics of process data and improve the prediction accuracy of pollutant concentration significantly. First, elastic net algorithm is used to comprehensively analyze the spatial correlation between the sample points and the target points, and reconstruct the spatiotemporal data, so as to provide the optimal variable input for the prediction model. Second, with the strong generalization ability of the Gaussian Process Regression model, the complex nonlinear characteristics of spatiotemporal data can be effectively handled, so as to obtain better prediction results. Finally, simulation experiments are carried out on AQI data set and air SO2 concentration data set, and the experimental results show that the prediction accuracy of the proposed method is more than 22% higher than that of the contrast methods.

Abstract:Due to the problem of pattern synthesis of the antenna array with targets lying in the near-field, a near-field pattern synthesis algorithm based on SPS-JADE algorithm is proposed. In this paper, based on the derivation and analysis of delay expression and pattern model of near-field pattern, with the element positions of linear non-uniform antenna array being the optimization variables and attaining beam focusing and sidelobe suppression being the optimization objective, the pattern synthesis in the near-field is transformed as an optimization problem. And an improved intelligent optimization algorithm, SPS-JADE algorithm is used to solve the problem and improve the optimization performance of pattern synthesis. The simulation experiments compared with other intelligent optimization algorithms are carried out, with the SPS-JADE algorithm performs, the average normalized maximum sidelobe level is lower than -22.1dB and -9.3dB in the angle and range dimension patterns, respectively, and the beam focusing is realized successfully, these results are superior to the other algorithms, which show that the algorithm has better near-field pattern synthesis ability than traditional methods. Keywords: antenna array; near-field; different evolution algorithm; sidelobe suppression

Abstract:Aiming at the shortcomings of traditional ant colony algorithm, such as easy to fall into local optimum and low precision of solution, this paper proposes an improved particle swarm optimization and ant colony algorithm to calculate the optimal path. In this algorithm, particle swarm optimization algorithm with linear decreasing inertia weight coefficient is used for path pre-planning, so as to obtain the initial pheromone distribution of ant colony algorithm. At the same time, by introducing new heuristic function, linear decreasing volatility coefficient and pheromone increment coefficient arranged according to path length into ant colony algorithm, the convergence speed of the algorithm is improved. Experimental results show that the path length error of the algorithm in two environments is 0% and 0.9297% respectively. Compared with the traditional algorithm, this algorithm has higher accuracy.

Abstract:To make 3D reconstruction suitable for various target shapes and improve the processing speed, a 3D reconstruction method based on machine learning is proposed, which focuses on solving the "next best viewpoint" (NBV) planning problem. Firstly, the definition and calculation of NBV are given, and the discrete NBV search space is established. Then, NBV is generated, and the space is reconstructed iteratively. In addition, in order to deal with the learning problem of NBV, a classification method based on 3D convolution neural network is proposed, which considers the possible position and pose of sensor as a classification problem. The experimental results show that the reconstruction accuracy of the proposed method is better than that of the voxnet network method, which can meet the constraints better. Compared with the high-precision information gain method, the proposed method also achieves better and close reconstruction coverage. Basically, it can achieve high coverage in 4 scans for different shapes, and the reconstruction speed is about 90 times faster.

Abstract:Aiming at the problem of system performance degradation caused by delay and packet loss in the network control system, a new delay and packet loss compensation method is proposed. First, establish a time-delayed network control system space model, transform the time-delay network control system into a non-delay system through linear conversion, and use autoregressive algorithm to predict the time delay online, and then give an improved reaching law The sliding mode controller compensates for the delay, and uses a zero-order holder with logic judgment function to deal with the packet loss problem. This method has better real-time performance in predicting time delay, and better system stability and robustness in dealing with packet loss problems. The simulation results on the Truetime platform show that this method has a better compensation effect on network delay and packet loss.

Abstract:In the visual inspection of personnel, the human position presents highly dynamic characteristics. For the situation that the robot can only capture the local information of the human body at close range leading to the inefficiency of passive vision detection, we propose an active vision method based on deep reinforcement learning. This method uses a deep convolutional network to extract image features and uses reinforcement learning strategies to train an action decision network to control the pan-tilt camera. Experimental results show that the algorithm enables the robot to turn the pan-tilt camera to achieve active face search based on the local information of the human body appearing in the image, which breaks through the limitations of the traditional method of passive detection and improves the adaptability of the visual detection algorithm in robot applications.

Abstract:The use of remote sensing images to monitor ships on the sea has become a hot spot in current research. In order to solve the defects of traditional ship detection that requires manual feature selection, time-consuming and labor-consuming, and the original YOLO algorithm has low detection accuracy for densely distributed small targets. This paper proposes a remote sensing image ship detection method based on YOLOv5, using the remote sensing data set provided by the Kaggle platform, training on the Pytorch framework, the loss function is designed as CIOU_LOSS, and the selection of the target frame uses the DIOU_NMS algorithm to make the occluded and overlapped The target detection effect is enhanced. After experimental comparison, the detection accuracy of this target detection model for occluded and densely arranged ships is better than other models, and its average detection accuracy is increased from the original 88.75% to 91.27%.

Abstract:Brain MRI images often have a lot of noise, and the edge is not clear, which makes the traditional fuzzy C-means (FCM) clustering algorithm can not obtain accurate brain tumor segmentation results. Therefore, a brain tumor image segmentation method based on Markov random field (MRF) and hybrid kernel function clustering is proposed. Firstly, particle swarm optimization is used to initialize the cluster center; Then, the single Gaussian kernel function in the traditional kernel fuzzy clustering algorithm (KFCM) is replaced by the mixed Gaussian kernel function; Finally, the prior probability of Markov random field is introduced to modify the objective function of the algorithm and enhance the anti noise performance of the algorithm. The experimental results show that the proposed algorithm has good anti noise performance in brain tumor image segmentation, and the segmentation accuracy is significantly higher than the traditional algorithm. The average values of dice index and Jaccard index are 0.9501 and 0.9051 respectively.

Abstract:The traditional correlation filtering algorithm is improved to improve the tracking performance of the algorithm in complex scenes such as scale change and occlusion deformation. In this paper, a robust tracking algorithm combining scale adaptation and re detection mechanism is proposed. Based on the fusion of FHOG and CN complementary features, a scale adaptive strategy is introduced to solve the problem of scale change. In addition, the model updating strategy is further optimized and the re detection mechanism is added to enhance the robustness of the algorithm. The otb100 dataset test results show that the accuracy and success rate of the proposed algorithm are improved by 4.9% and 17%, respectively, compared with KCF algorithm. The average tracking speed is 45 frames / s, and the performance is excellent in occlusion, scale change and illumination change scenes, which can effectively achieve long-term target tracking.

Abstract:In the traditional region growing algorithm, because of the limitation of the growing rule, it is easy to produce over segmentation phenomenon in medical image segmentation. In order to solve the problem of over segmentation, a region growing method based on distance constraint and multiple sub points weighting is proposed to segment the cochlear region in temporal bone MR images. Firstly, a distance constraint is added to the region growing method to solve the over segmentation problem in the original algorithm. Then, the dynamic multi seed weighted growing rule is introduced to reduce the error caused by the unsuitable selection of seed points. After experiments on several groups of data, the average values of Dice coefficient and intersection union ratio of the improved algorithm are 90.13% and 88.59% respectively, and the average values of area overlap error and relative area difference are only 11.64% and 9.89%, which are better than other algorithms.

Abstract:In the process of automatic strip steel surface defect detection, due to the influence of the camera itself and the camera environment, the acquired images will have uneven brightness, which has a greater impact on the subsequent defect image segmentation and recognition. Based on this, this paper proposes a brightness unevenness correction method based on improved homomorphic filtering, which combines the characteristics of strip surface defects to reselect the cutoff frequency of the filter, and uses different filter functions for the high and low frequency components of the strip image. After processing, the processed high and low frequency components are used to reconstruct the brightness-corrected image. The experimental results show that the improved homomorphic filtering in this paper has higher image quality after brightness correction. The image quality parameter Q value after brightness correction is used to evaluate it. The average Q value of the method in this paper reaches 0.9014, which is higher than the parameter values of other methods. high.

Abstract:in order to strengthen the protection of ancient buildings and avoid "secondary damage" to ancient buildings, gls-2000 3D laser scanner collects the information of ancient buildings, uses improved ICP algorithm to realize the point cloud data splicing of ancient buildings, Geomagic software to realize the point cloud data denoising and simplification, and Revit software to extract the feature lines and 3D modeling of ancient buildings. Finally, taking the roof components and the gallery components of ancient buildings as examples, the application effect of bim scanning technology is analyzed, and the application effect of the improved ICP algorithm is tested. The results show that bim scanning technology can accurately determine the size information of ancient buildings, establish 3D model of ancient buildings, and effectively preserve the information of ancient buildings. The registration time of the improved ICP algorithm is 5.623 seconds, the registration accuracy is 4.21 λ / mm, the average error of point cloud 3D model is 0.113 m, and the mean square error is 0.154 M,and the application effect is good.

Abstract:Aiming at the problem that the pilot aided channel estimation of OFDM system in fast fading environment is greatly affected by channel state fluctuation, a channel identification and compensation method for OFDM system based on artificial neural network is proposed. Firstly, the traditional decision feedback channel estimation method is analyzed, and its problems in fast fading environment are explained. Secondly, the decision feedback channel estimation is used to obtain part of the channel state information at constant intervals, so that only a small amount of estimated channel state information is used to train the artificial neural network. Then, Levenberg-Marquardt algorithm is used for neural network training. Finally, after the training of the artificial neural network, all data symbol indexes are serially input to the artificial neural network, so as to interpolate the whole transition of the channel state information, thus effectively compensating the channel variation. The test results of OFDM communication system in fast fading environment show that, compared with traditional estimation methods, this method has better BER performance in high mobility environment with Doppler frequency of 700 Hz, and can eliminate error flat layer, and the BER is below 10-4.

Abstract:Aiming at the problem of abnormal data in the application of smart meters, by defining any abnormal power consumption instance or trend beyond the normal use mode of each load, a data-driven model based on different types of characteristics such as load, context and environment is designed, and four different unsupervised models based on regression, neural network, clustering and projection are evaluated The performance of the learning method in the actual smart meter data anomaly detection is analyzed. The results show that different anomaly detection methods have different detection ability for different types of anomalies, and their performance depends on the feature set used to train the method. Therefore, for each anomaly detection method, different types of features should be carefully examined.

Abstract:Aiming at the problem of large startup initial mover position detection error (+/-30° electrical angle) of the switch Hall method for the permanent magnet linear servo system, a new pulsating high-frequency voltage signal injection and forward-reverse voltage pulse vector injection combined method was proposed. By demodulating the high-frequency current response signal, closed-loop position resolve and phase compensation, the rapid and high-precision initial mover position detection is achieved. The simulation results show that the proposed method can realize rapid initial mover position detection, and the error does not exceed +/3° electrical angle. The detection error is reduced by 90% compared with the Hall initial position detection method, then the startup smoothness of the permanent magnet linear servo system will be improved further.

Abstract:In response to the needs of modern greenhouse planting technology, a design that can monitor the temperature and humidity changes in the greenhouse in real time, and display the results on the computer in real time, so that users can always understand the changes in temperature and humidity inside the greenhouse. The system uses DS18B20 to collect temperature data; uses HR202L to collect humidity data, uses XC7Z020 as the logic control core to frame the collected temperature and humidity data, and sends the processed data to the Qt host computer through the serial bus, and Qt processes the received data. Visual processing, real-time display of the temperature and humidity changes in the greenhouse in the form of an interface, and corresponding adjustments to the temperature and humidity in the greenhouse according to the threshold set in advance. Experiments show that compared with traditional human labor, the system can effectively reduce costs, increase the yield of crops in the greenhouse, and increase people's economic benefits.

Abstract:This paper provided a method to improve the accuracy of infrared temperature measurement on pavement by an accelerated K-means clustering separation interference signal based on the mean-standard deviation for initial central，using the temperature difference between the road surface and the vehicle，the standard deviation of the vehicle signals，and the duration of the interference signal to separate vehicle interference, low-speed obstacles, and environmental mutations data and retain environmental mutation data. The recognition rate of this method for interference signals is 95.6% on sunny days, 90.9% on cloudy days, and 78.2% on cloudy and rainy days, which effectively improves the accuracy of on-surface infrared temperature measurement on the road surface.

Abstract:Aiming at the problem of short service life of the sensor and low anti-electromagnetic interference ability. In this paper, an inductance-capacitance separation sensor is designed. This structure not only prolongs the life of the reading coil, but also reduces the interference of the parasitic capacitance of the inductor and the resistance of the reading coil to the sensitive signal, and also improves the sensitivity of the sensor. The performance of the sensor was tested in the temperature range of 100℃⁓1300℃, and the results showed that the coupling distance between the sensor and the coil antenna was within 10mm, and the resonant frequency of the sensor shifted to the left regularly with the rise of temperature, and the measurement sensitivity was as low as 2 kHz/℃, up to 7 kHz/℃. It can be seen from the test results that the sensor can be applied to temperature monitoring in harsh environments and the test results are reliable.

Abstract:Affected by slenderness ratio of drilling string, anisotropism of formation, unstable parameter of operation, drilling bit suffers a complex vibration of six degrees of freedom with multiple dominant frequency, dimension and amplitude. It seriously restricts the safety and efficiency of drilling operation. According to the theory of rigid motion, the angular velocity reckoning is difficult by the current planar array scheme, and central fluid channel cannot be reserved by the conventional space array. In this paper, a spatial array with a central fluid channel was proposed. Least squares and neural network was also established to reckoned the vibration parameters. The average error of reckoned vibration parameters is less than 5×10-3, which meet the application requirements of drilling operation. The proposed method and array scheme provide the data base to the vibration conditioning of drilling string, and contribute to the research of sensor vibration test.

Abstract:In view of the problem that the existing resistive sensor conversion circuit design mostly adopts the bridge circuit and its circuit structure is complicated, the design idea that the bridge circuit can be replaced with a very simplified circuit in certain application scenarios is proposed. By comparing and emulating these two types of circuits, it is pointed out that extremely simplified circuits can also meet the application of most scenarios with the continuous improvement of microprocessor performance and analog-to-digital conversion circuit resolution. So that the cost of raw materials can meet the requirements of functional performance. Then, push to a lower limit under the circumstances. On this basis, an optimal device selection method for extremely simplified circuit of fixed-value resistors is proposed. Finally, the actual bluetooth thermometer project is used for development and design . The test data shows that the temperature measurement error is less than or equal to 0.1℃, which meets the requirements of national standards.

Abstract:The tuning characteristics of diode lasers in TDLAS (Tunable Diode Laser Absorption Spectroscopy) technology is one of the factors that restrict the measurement performance of the system. Aiming at the inherent wavelength tuning nonlinearity of current DFB lasers, a tuning nonlinearity calibration method based on polynomial fitting current pre-calibration algorithm was proposed. Firstly,the laser current wavelength measurement system was built, the current wavelength relationship was measured and the linearity was analyzed, and the injection current pre-calibration mathematical model was deduced based on polynomial fitting algorithm. Secondly, the fitting effect of polynomial fitting algorithm was analyzed. By comparing MSE value (mean square error) and convergence characteristics of each order fitting polynomial, quadratic polynomial was selected as calibration functions. Experimental results show that compared with the uncalibrated state, the pre-calibration algorithm based on polynomial fitting current greatly reduces the calibrated MSE value. Among them, the laser tuning nonlinear error analysis was performed on the better second-degree polynomial fitting algorithm. Compared with 15.39% before calibration, it was reduced to 2.27% after calibration, it was verified that the calibration method can greatly improve the linearity of current wavelength tuning of DFB semiconductor lasers.