Abstract:To solve the problem that there is no rule to unify time for wider space such as solar system, a new rule for time uniformity is proposed in this paper. Space time-keeping system (STKS) evolves from the concept of Space Metrology which is based on the theory of General Relativity, and meets the convention of time unit and the beginning of time. It uses the cesium atomic clock to measure proper time and pulsars to measure coordinate time, unifying time through the coordinate time on the origin of solar system barycentre coordinate. The viewpoint of relative time denies the uniqueness of standard time. For different local area or coordinates, when looking from each other, one’ s measurement of the other’ s time interval would be uneven, showing a curved coordinate axis. While on the viewpoint of absolute time, the standard time is unique and different timing devices could be synchronized by dissemination technology. The concept of STKS will revolutionize the traditional viewpoint of absolute time. With a customized feedback mechanism designed, it would improve time keeping technology to a more stable scale.

Abstract:With the continuous development of sensing technology, more and more sensor-based wireless sensor detection systems have emerged. These sensor systems require data analysis of the collected data. Therefore, the data analysis of sensors plays a vital role in the accurate detection of wireless sensing systems. Firstly, the design SAW micro-pressure sensor is measured, and the mathematical model is established by the least square method,and the relationship between the measured frequency change and the corresponding load force is analyzed by data fitting. Subsequently, a BP neural network model was constructed to train the collected data and predict the relationship between the input and output of the SAW pressure sensor. Finally, genetic algorithm is used to optimize the BP neural network. The results show that after optimization, the error of BP neural network is reduced by nearly 45% compared with that before optimization, thus verifying the feasibility of genetic algorithm to optimize BP neural network. This method provides an important research basis for the development of wireless sensor detection system for acoustic surface wave micro-pressure sensor.

Abstract:Aiming at the problem of single parameter of traditional flow meter and problem of composite measurement of fluid flow and temperature, a composite flow and temperature sensor based on fiber Bragg grating is proposed. The pressure loss of fluid is reduced by using hollow cylindrical cantilever beam. The end cover, cantilever beam and target plate are processed in one body, which increases the stability of the sensor Firstly, sensing theory of fiber Bragg grating and flow-temperature are combined. The flow and temperature composite sensing theory based on fiber Bragg grating is studied. Then, the sensor that integrated target structure with the cantilever beam of hollow cylinder is designed. And its performance is simulated. Finally, the flow and temperature experimental system platform is set up. The sensing of temperature and flow experiments are carried out on the sensor. The results show that the relationship between temperature and wavelength shift is linear. The linearity is above 99%. The temperature resolution of the sensor is 0. 04℃ , the temperature measurement error is 1. 8%, the flow resolution is 0. 33 m 3 / h, and the flow measurement error is 2%. The fiber Bragg grating composite sensor can realize composite measurement of flow and temperature, and is expected to be applied to a flow measurement system requiring large-scale network detection, and has a good application prospect.

Abstract:In a large maneuvering scene, the location estimation error and the replenishment frequency of stars occur. To solve these problems, a star tracking algorithm assisted by gyroscope is proposed in this work. In a large maneuvering scene where the angular acceleration of the carrier changes dramatically, the angular velocity information measured by gyroscope is invested to keep track of the identified stars. Thus, the center-star-points of two-consecutive frames are easily obtained. The motion model of star points is established, and the position of the star points are tracked and predicted by Kalman filter. The positions of stars inside and outside the star map can be predicted in advance. The cache star database proposed in this paper can realize fast star replenishment in a large maneuvering scene. Experiments confirm that the error of star tracking is less than 2-pixels. Furthermore, the cache database reduces time of star replenishment. Both results prove that the proposed algorithm is effective, and conduces to improve the dynamic performance and update rate of star sensor.

Abstract:Based on additive manufacturing technology, this paper studies and fabricates a new FBG pressure sensor by changing additive manufacturing parameters including infill densities, infill model sizes, and infill materials. The test results show that the shrinkage strain and tensile strain of infill materials change evenly during encapsulation of FBG sensor. Three functions including the upper bound function-f(x)= -9E-08x 3+0. 000 4x 2-0. 709 8x+368. 86, the lower bound function-f(x)= -1E-08x 3 -3E-05x 2 +0. 096 4x-235. 95, and the residual function-f(x)= -5E-09x 3+1E-04x 2-0. 653x+712. 9 are defined respectively in term of the strain characteristics inside the printing model. The calibration test and stability test of the sensor prove that the FBG pressure sensor has good linearity and stability. When the infill densities of the FBG pressure sensor was 20%, 40%, 60%, 80% and 100% respectively, and the infill material and infill size were kept the same as the Carbon Fiber material and the cube model (35 mm×35 mm×10 mm), the sensor’s sensitivities was found to be 0. 69, 0. 45, 0. 39, 0. 21 and 0. 19 pm/ kPa, respectively, the sensitivities decrease with the increase of infill density. When the infill model is cylinder with diameter of 60 mm and thickness of 6 mm (60 mm×6 mm) and the cylinder (60 mm×15 mm) respectively, and the infill material and infill density was kept same as Carbon fiber and 20% respectively, the average sensitivities of FBG pressure sensors with thickness of 15 mm and 6 mm respectively are 0. 59 and 0. 18 pm/ kPa respectively. When the infill material is carbon fiber (CF) and Polylactic Acid (PLA) respectively, and the infill density and infill size was kept same as 20% and cylinder (60 mm× 15 mm) respectively, the average sensitivities of FBG pressure sensors made by PLA and CF respectively are 16. 26 and 0. 59 pm/ kPa respectively. Therefore, FBG pressure sensors of different sensitivity can be obtained by changing the infill density, infill material, and the size of infill model.

Abstract:Surface acoustic wave ( SAW) micro-force sensor has the characteristics of easy integration and miniaturization, passive wireless working mode, and has great application potential. Applying it to measure the micro-gap pressure between objects is expected to achieve a high-sensitivity, low-power monitoring level. Aiming at the problem of measurement sensitivity in complex environments, SAW micro-force sensor with a broken-line interdigital transducer (IDT) is designed. The finite element analysis method (FEM) is used to carry out simulation analysis and structural parameter design. Theoretical analysis and experiments show that the mass sensitivity of the designed sensor can reach 284. 16 kg / m 2 , which is higher than the linear IDT SAW sensor with the mass sensitivity of 185. 97 kg / m 2 . The results verify that IDT structure can effectively improve the mass sensitivity of the device and the feasibility of monitoring the pressure in a micro-gap environment.

Abstract:In order to accurately study the influence of calibration temperature on frequency domain reflectometry (FDR) capacitive soil moisture sensor measurement, to improve the consistency of the data of network measurement sensors deployed in different sites. According to the measurement range of capacitive soil moisture sensor, the temperature effect of reference frequency experiment scheme is proposed. The relationship between the frequency of the sensor and temperature in air and water media is obtained by experiment, and the relationship between air temperature and water temperature is also obtained through the experiment. Furthermore, the relationship between normalized frequency and water media temperature is derived with water temperature as independent variable. According to the air frequency temperature fitting equation, the number of frequency reduction 4. 860 5 Hz on average when the air media temperature rises 1 ℃ ; and according to the water frequency temperature fitting equation, the number of frequency increases 6. 615 5 Hz on average when the water media temperature increase 1 ℃ . There is an inflection point between the normalized frequency and the water calibration temperature function, When the soil moisture is higher than the inflection point, the normalized frequency increases with the increase of temperature, The higher the soil moisture is, the higher the normalized frequency. When the soil moisture is lower than the inflection point, the normalized frequency decreases with the increase of temperature, the lower the soil moisture is, the higher the normalized frequency reduction rate. Secondly, calibrate the soil moisture sensor with two typical texture undisturbed soil sample. Based on the calibration curve of the sensor, the relationship between the volumetric water content of undisturbed soil and he calibration temperature is derived. The result show that the maximum influence error of calibration temperature on the measurement of volumetric moisture content of undisturbed soil is 0. 12% cm 3·cm -3·℃ -1 , The measurement error of volume content in the temperature range of experimental is 5. 93%(cm 3·cm -3 ).

Abstract:Based on the analysis of soil dielectric constant,the measuring principle of capacitive soil moisture sensor is studied. This paper uses ANSYS software to establish a three-dimensional model of a capacitive moisture sensor probe, and studies the influence of the sensor on its capacitance value, electric field intensity distribution, moisture content detection depth and sensitivity when the structural parameters of the sensor probe change. Optimizing size parameters, a capacitive soil moisture content sensor based on frequency domain reflection method is designed. The simulation experiment results show that when the probe ring electrode radius is 28 mm, the axial width is 28 mm, the electrode spacing is 12 mm, and the electrode thickness is 1 mm, the sensitivity, signal measurement strength and detection range of the probe are significantly improved compared with the control product. It is a capacitive soil The structural design of the moisture content sensor provides a theoretical basis.

Abstract:Due to the scattering and absorption of water medium and underwater particles, there are some problems in underwater image, such as low resolution and contrast, fuzzy details, color distortion and so on. Aiming at these problems, this paper proposes an underwater image sharpness enhancement algorithm based on light field imaging. According to the dark priori theory and the single-scale Retinex theory, the underwater scattering imaging model is established to enhance the sharpness of the image, and the image enhancement effect of the scattering imaging model is optimized by using the multi angle characteristics of light field imaging, so as to further improve the quality of underwater imaging image. The experimental results show that the underwater scattering model and multi view optimization algorithm can effectively improve the quality of underwater image.

Abstract:In order to complete the spaceflight docking mission, address the problem of measuring the docking hole position under complex illumination conditions, design a docking hole position measurement system with dual band and dual channel based on the heterogeneous. The binocular distance measurement system of light source and its distance measurement error compensation method are studied. After introducing the binocular ranging and error compensation principle of heterogeneous light source, according to the visible, near-infrared and binocular ranging characteristics and error analysis results, using neural network to fuse and compensate the data from different light sources to achieve position measurement of docking holes under weak light, strong light and no light conditions. Through the experimental test, the results show that in the measurement range of 0 ~ 10 m, the relative measurement error of the system under normal light conditions is basically in the within 1%, the relative measurement error under low-light conditions is basically within 2%, which is 8% ~ 9% and 4% ~ 5% higher than that of monocular and binocular distance measurement respectively. The function of higherprecision distance measurement under different lighting conditions is better realized.

Abstract:Aiming at the problems of low contrast, uneven background and halo artifacts in the images of mesenchymal stem cells collected by the phase contrast microscope, this paper proposes a cell image segmentation method combined with anti-background subtraction and Otsu. The method constructs anti-background subtraction to enhance the difference between the cell body and the noncellular area and reduce the influence of uneven background, combines the Otsu threshold segmentation method to roughly distinguish the cells and the background, and further corrects the segmentation results by a combination of algorithms including binary morphology operations, image filtering, and local gradient iteration. The four evaluation indexes of pixel accuracy, intersection over union, dice similarity coefficient, and confluency error achieved values of 0. 933 8, 0. 729 6, 0. 852 4, and 0. 07, respectively, by segmentation validation on the actually acquired cell images. The results indicate that the algorithm has high segmentation performance, can objectively, accurately and automatically analyze the confluency of cells, and can process images of cells in different culture periods, which has high application value.

Abstract:CNN-SVM hybrid algorithm combines the feature extraction ability of CNN and the classification performance of SVM, it has certain advantages in computational complexity and can solve small sample problem. It has been applied in fault diagnosis, medical image processing and other fields, at the same time, it gets attention in the field of edge computing due to its low computational complexity. Aiming at the requirements of algorithm performance and power consumption in edge computing scenarios, an optimization and implementation method of CNN-SVM algorithm for FPGA platform is proposed. First, combined with the architecture characteristics of FPGA, the hardware adaptability optimization of CNN-SVM algorithm structure is carried out, including the model compression and the selection of kernel function of classifier. Secondly, the design and implementation of CNN-SVM algorithmic accelerator is completed by using software and hardware cooperation and high level synthesis ( HLS) design method. The experimental results show that on ZCU102, the frames per second(FPS) of accelerator reaches 18. 33 K, the computing speed is 1. 474 GMAC/ s. Compared with the CPU platform, quad core Cortex-A57 and Ryzen7 3700x achieve 23. 57 and 4. 92 times acceleration respectively, compared with Jetson Nano GPU and GTX750 platform, the energy consumption ratio is 33. 24 and 50. 27 respectively.

Abstract:Aiming at early fault prediction problem of connected-grid PV converters, a fault prediction method is presented based on RLSSVR in this paper. The degradation parameter couplings of vulnerable components and its effects on conditions of converters and selection of features for fault prediction are analyzed in this paper in system level, and then the prediction method using the relative variables of features as converter states is proposed. In order to reduce the degeneration prediction from the working condition, the method firstly builds the feature fitting model without degeneration with work condition as input and state features as output by using robust least squares support vector regression (RLS-SVR). Then, the time series of relative variable features of converters is obtained by combining the online working conditions time series and feature time series with no degeneration fitting model during the degradation procedure. At last, the prediction model of the relative variable time series of features of converters is built based on the degradation time series and using RLS-SVR. The prediction method is simple, low cost, high precision, and without adding other sensors. Experimental results of singlephase PV connected-grid converter show that the proposed method is feasible and effective.

Abstract:Bolts are the most commonly used connectors for mechanical equipment. The stability of bolt connection plays an important role in ensuring the safe operation of mechanical equipment. It is of great significance to detect the state of bolt looseness. Aiming at the four different states of bolt loosening, a bolt looseness detection method based on variational mode decomposition (VMD) and timefrequency sensitive feature combined with least square support vector machine (LSSVM) is proposed in this paper. In order to identify the four different states of Bolt looseness, a simulation experimental platform for bolt loosening detection is built, and the vibration response data of four different states of bolt looseness are obtained by accelerometer. The time-frequency sensitive features are extracted, and the IMF component energy entropy decomposed by VMD is combined to form the sensitive multi-feature vector. The extracted multifeature vectors are combined with least square support vector machine to detect different looseness states of bolts. The recognition results are compared with the results of empirical mode decomposition ( EMD)-LSSVM and EMD multi-feature-LSSVM recognition. The recognition rate of bolt looseness detection method based on proposed VMD multi-feature in this paper is better than that of EMD-LSSVM detection method.

Abstract:With rapid development of internet of things ( IOT) and information technology, mobile location-based service ( LBS) is gaining more and more research focus in recent years. It also stimulates the development of indoor localization technology. Owing to the advantage of its pervasive deployment, WiFi fingerprint-based indoor localization has drawn much attention to academia. However, the fluctuation of WiFi signals and other interference always influence the localization performance. In this paper, a hierarchical deep learning indoor localization framework (HDLIL) is proposed to solve the problem of mobile device localization in indoor environments and predict the specific position. To capture and learn the reliable fingerprint features, a feature extraction module based on variational autoencoder (VAE) is introduced to characterize the latent representation of the training data. Also, to train the localization model, we feed the reconstructed training fingerprints as well as corresponding labels to a 3-layer deep neural network, of which the output of current layer is set as the input for the next layer, followed by a location output module based on the concatenated softmax classification. In the localization phase, the localization fingerprints ( testing fingerprints) is set as the input, the HDLIL model is invoked and the output of final layer is the predicted location of the mobile device. In addition, to evaluate the localization performance, we conducted the experiment in a real indoor scene, and several influence factors are discussed. The result indicates that the proposed HDLIL model can attain a superior localization performance.

Abstract:To improve the operational efficiency and measurement accuracy of the dynamic check weigher, the interference of mechanical vibration to the measurement and the generating mechanism of the sensor’s nonlinear characteristics are deeply analyzed. A multi-layer BP neural network based on ADAM optimizer is proposed to realize the nonlinear correction of weighing sensor and estimate the dynamic weighing results accurately. The classical gradient descent algorithm, gradient descent algorithm with momentum and root-mean-square propagation algorithm are compared with the ADAM algorithm through experiment. According to the results, the ADAM algorithm had faster convergence speed and more accurate prediction results as it comprehensively considered the first and second sample moment of parameter’s gradient. The high speed dynamic check weigher with full range of 400 g and maximum running speed of 2 m/ s is manufactured, The type test results showed that all of its indicators meet the requirements of national standard GB/ T 27739 - 2011 automatic divider for XIII check weigher.

Abstract:In order to study the damage evaluation of specimens mainly subject to bending in steel structure buildings, a three-point bending test was conducted on the Q235 steel plate specimen to collect the magnetic memory signal value Hp(y) during the loading stage of the specimen, multiple magnetic memory signal characteristics are introduced to characterize the damage of steel structures. The experimental results show that the stress concentration area can be determined by the phenomenon of “ peak” and “ trough “ of the magnetic memory curve and the phenomenon of“peak-peak” of the magnetic memory gradient curve. It can be found that the tensile of the specimen is sensitive to the yield of the specimen through other relevant characteristic quantities ΔHp curve, Sh and | K | max curve. The “inflection point” of the ΔHp -F curve and the “ peak” of the | K | max -F curve are used as the basis for the specimen to enter the yield. The study is applicable to the prediction of structural damage characterization, and the feasibility of magnetic memory detection in steel structure damage is verified by multiple parameters.

Abstract:As the excitation signal in the bioelectrical impedance measurement system, the constant current source needs to output a wide frequency band and stable current. In this paper, a program-controlled broadband constant current source design scheme is proposed: With STM32 as the control core, sinusoidal voltage signal with adjustable frequency is generated though the DDS. Tietze current pump a current conversion is performed with wide frequency bandwidth and small distortion. By the MCP41010 digital potentiometer, a numerical control gain amplifier circuit is designed to solve the problem of high frequency attenuation of constant current source. Based on the simulation experiment proved that the constant current source has excellent output characteristics with frequency and load changes, the constant current source transposition was developed. Experimental data shows that the controllable output frequency range is 0 ~ 1 MHz, the load capacity range is 0 ~ 5. 4 kΩ. The average error of the current effective value changes with frequency and load is 0. 137% and 0. 251% respectively. Proved that it has the characteristics of wide output frequency band, strong load capacity and stable current output which can meet the needs of bioelectrical impedance spectrum measurement.

Abstract:The image splicing localization algorithm based on dual-stream Faster R-CNN achieves a good performance because it considers both the color image and its corresponding noise image as inputs. However, it still has the following two drawbacks, it only achieves block-level precision and the noise images generated by SRM filter are likely to contain a lot of redundant non-forged semantic features. Therefore, this paper proposes a pixel-level image splicing localization model based on dual-stream Faster R-CNN. Regarding the first drawback, a fully convolutional neural network branch is added for pixel-level localization. Regarding the second one, the steganalysis rich model is replaced by error level analysis noise model for noise map extraction. Experimental results show that the proposed algorithm improves the accuracy by nearly 10% compared with some existing algorithms.

Abstract:Complete edge information is essential to the detection of edge defects of turbine wind turbine blades. Due to the complex and diverse background of wind turbine blade ( WTB) images, the existing image segmentation algorithms have insufficient segmentation accuracy and cannot guarantee the integrity of edge defects. Therefore, an adaptive image segmentation based on image edge features and color information is proposed for the edge detection of WTBs. Firstly, Hough line detection is used to detect the blade edge at straight lines. Secondly, the Graph-cut algorithm based on Otus threshold segmentation and morphological operations is applied to adaptively separate the blade target areas. Finally, comparative experiments are carried out with a lot of image samples captured under various scenes. The results show that higher edge coverage and lower boundary displacement errors of the WTB image segmentation with 0. 971 7 and 3. 040 3 are obtained by the proposed method as compared with other image segmentation methods. The proposed approach has potential application value for the edge defect detection of WTBs.

Abstract:The early weak fault diagnosis of check valve can prevent economic loss and safety accident caused by the failure of the high pressure diaphragm pump due to the wear and breakdown of check valve. Aiming at the problem that early weak fault feature of the check valve of the reciprocating high pressure diaphragm pump are not obvious and are disturbed by a lot of noise, Method: a method for early weak fault diagnosis of check valve based on composite multiscale fluctuation dispersion entropy (CMFDE) is proposed. First of all, replace the normal cumulative distribution function mapping in the CMFDE method with tangent sigmoid to improve the noise resistance of CMFDE. Then, calculate the composite multiscale fluctuation dispersion entropy of the vibration signal, and construct the feature matrix and input it into the support vector machine (SVM) classifier for fault diagnosis. Finally, the actual engineering data of the check valve is used to verify the effectiveness of the method, and a comparative experiment is carried out. The experimental results and comparative analysis show that it is not necessary to reduce the noise of the original signal of check valve, which simplifies the diagnosis process. The composite multiscale fluctuation dispersion entropy feature can accurately reflect the different signal characteristics of check valve, which improves the identification rate of the early weak fault diagnosis of check valve, and the fault diagnosis result is less affected by classifiers, with the identification accuracy reaching 96. 667%.

Abstract:In order to further improve the de-noising ability of conventional polynomial matching algorithms, an improved polynomial matching filtering algorithm based on edge protection is proposed, aiming at the details such as image edge / texture easily blurred by conventional polynomial matching algorithms. Based on the conventional polynomial matching algorithm, it improves the selection method of filtering window, extracts adaptive sliding filtering window along the direction of image texture direction, selects the window with minimum matching error for matching filtering and takes it as the final output result. Then the grayscale image and the actual CT image were tested respectively. The data verification shows that this method can maintain edge / texture information while effectively suppressing the quantization of noise, and the peak signal-to-noise ratio is increased more than 80%, and the root mean square error is reduced more than 80%. Compared with the conventional polynomial filtering method, median filtering method, bilateral filtering method, edge preserving filtering method, BM3D and DnCNN method, the improved method can effectively improve the image visual effect, meet the requirements of image application, and has a good application prospect.

Abstract:To solve the problem of large fluctuation of braking current in the regenerative braking control of switched reluctance motor, a regenerative braking control method based on current prediction is proposed. Motor brake are analyzed in the process of the work of the corresponding power switch mode and the method for determining the corresponding state vector, is studied based on the current forecast of switched reluctance motor regenerative braking control of the specific design method, and the effect of simulation verification, and compared with the traditional variable braking phase voltage duty cycle control method compares the simulation analysis, the results show that this control method is compared with the traditional variable braking phase voltage duty cycle control method significantly reduces the motor current braking and braking torque fluctuation degree, thus effectively improve the stability of the braking operation and has good application value.

Abstract:The existing step counting algorithms cannot effectively solve the problem of the diversity of user walking patterns and mobile phone holding styles as well as the problem of if the detected step is an actual step or a mimicking behavior. Therefore, an adaptive time window step counting algorithm based on peak detection is proposed. The algorithm counted the steps by detecting and verifying, used double filtering to preprocess the original acceleration, and designed an adaptive time window based on the peak and valley time difference to eliminate false peaks, used the variance and standard deviation to verify the peak, finally. The experimental results show that compared with traditional methods (fixed window peak detection, conditional judgment peak detection), the average step-counting accuracy of this algorithm in different motion states and different mobile phone holding styles is increased by 7. 7% and 3. 4%, respectively, and is better than the current popular commercial step detection application.

Abstract:Using a loop formed by steel cableway and water body is effective to communicate across the media, under water to above water. But this communication method is easily affected by environment and has poor communication quality. To improve the communication quality, the transmission characteristics and influence factors of electrical signals in the loop are studied. The equivalent circuit of communication channel is proposed according to the water body characteristics. Electrical signal transmission path and electric field distribution are analyzed by finite element simulation. The influence of the actual factors such as the communication distance, the depth of the electrode plate into the water, the width of the water body, the size of the electrode plate, the conductivity and the dielectric constant on the equivalent circuit parameters are simulation calculated. The results show that the communication distance, the plate size and the conductivity are the main influence factors of signal attenuation. When the communication distance and the plate size increase, the signal amplitude ratio is stable at 92. 2% and 95. 8%. A communication under water to above water long-distance can be realized fast and effectively by controlling the plate size and the signal frequency, which provides a theoretical reference for the design and optimization of underwater loop communication systems.

Abstract:Joint scanning thermography(JST) can detect defection of large-area materials. The defection of raw images is inaccurate and the quantitative analysis is hard to achieve. According to the characteristics of images from the reconstruction of joint scanning thermography, a method based on an one-dimensional convolutional neural network ( 1D-CNN) is proposed to detect and quantitate defects. The one-dimensional temperature time series corresponding to the pixels of the pulse image sequences is applied as inputs for the network. This method could achieve defect detection automatically and defect quantification for carbon fiber reinforced polymer. As the result indicated, the 1D-CNN based method could detect defection automatically and accurately. It has a 98. 8% accuracy in defect classifying of training set and an about 70% accuracy in defect classifying of training set. The result is better than traditional method.

Abstract:The accurate estimation of the rotor position during the start-up process of permanent magnet synchronous machine(PMSM) sensorless control has an important influence on motor control. Based on this, this paper proposed PMSM sensorless control method based on double predictive control. This method used a second-order generalized integrator instead of a band-pass filter as a highfrequency current extractor, and used model speed predictive control and deadbeat current control to replace the PI controllers in the speed delay and current delay to compensate for the feedback signal. Simulation experiments show that this method can reduce the error estimated when the motor start, and improve the dynamic performance of the motor starting process compared with the traditional sensorless control.

Abstract:This paper analyzes and designs a set of high-precision missile-borne pressure for complex environment temperature in response to the problem that the sensor is susceptible to the complex and changing test environment during the missile-borne pressure test process, resulting in a large deviation between the test results and the actual value. The test system was tested and tested. The test results were compared with the output of the conventional pressure test system. The results showed that the maximum relative error of the full-scale range of the missile-borne pressure test system was 0. 025 6, which was the largest for the conventional pressure test. 1 / 4 of the error. In order to meet the higher accuracy test requirements, the improvement of the original test system was completed by means of secondary compensation, and analyzed the system test results before and after the improvement. The results showed that the maximum relative error of the full-scale of the improved test system is reduced to 0. 004 8, and the test accuracy is improved by an order of magnitude compared with before the improvement.

Abstract:A pressure measurement method for in-service vessel based on magneto-mechanical effect is proposed. Non-intrusive and noncontact pressure measurement are realized by using the stable correspondence between magnetic signals outside the vessel and the stress on the vessel wall. The relationship between magnetic permeability and stress of steel in weak geomagnetic field is analyzed by using J-A coupling model, and the feasibility of using external magnetic field of vessel to measure internal pressure is theoretically proved. A multichannel synchronous magnetic signal acquisition system is designed, which can simultaneously acquire the magnetic signals of three components of the multi-sensor. Experiments are carried out to verify the performance and advantages of this method. The results show that in the range of 0~ 3 MPa, there is a good linear relationship between the magnetic field near the surface and the internal pressure of the vessel. The sensitivity of the pressure change of the magnetic field at different parts of the vessel surface is different, so multi-point deployment and calibration optimization are required. For low carbon steel pressure vessel with an outer diameter of 275 mm and a wall thickness of 7. 5 mm, the sensitivity of magnetic measurement can reach up to 131. 4 mGs/ MPa. Via axis-symmetrically arranging a pair of sensors and adding their output up, the influence of rotating vessel on measurement accuracy can be significantly weakened.