Abstract:Due to the influence of crosstalk, AC-Stark effect and other factors, the spatial position ( three-dimensional coordinates and sensitive axis pointing) and gain coefficient of the SERF atomic magnetometer are deviated, which directly affects the accuracy of magnetic source positioning. To address the above problems, an adaptive calibration method of SERF atomic magnetometer parameters is proposed. Based on the magnetic dipole model, a calibration device is designed. It consists of 24 precision-machined circular coils, which are used to apply the calibration magnetic source. An improved adaptive elite genetic algorithm is proposed to simultaneously calibrate the relevant parameters of the magnetometer. Experimental results show that the average correlation coefficient between the actual magnetic field curve and the theoretical magnetic field curve fitted by the algorithm is 99. 55% , the x-axis coordinate value drift is the most obvious, the average absolute deviation is 2. 63 mm, and the average absolute deviation of the sensitive axis is 8. 21 °. Results show that the sensor parameters need to be accurately measured before the magnetic source can be located. The proposed calibration method has certain reference significance for improving the positioning accuracy of the magnetic source.

Abstract:A high-accuracy estimation method for the rise time of shock tube reflection step pressure is proposed. First, a theoretical model for estimating the rise time of shock tube reflected step pressure is formulated, which is based on the step response characteristics of the pressure sensor. Secondly, the empirical mode decomposition is used to eliminate the noise components of step response signal, and the attenuation ringing component is extracted. Thirdly, the first peak time and oscillation period of the attenuation ringing component are estimated based on an attenuation sine fitting. Finally, the high-accuracy estimation of the rise time of shock tube reflection step pressure is realized according to the theoretical model. Simulation results show that the average relative error of the rise time estimation of step pressure is 2. 57% , which is about 7. 3 times and 2 times lower than those of the traditional method and the direct fitting method, respectively. Shock tube experiments show that the mean of the estimation values of rise time are separately 0. 712 μs and 0. 876 μs, and the measurement relative errors are 0. 14% and 1. 9% under the cases of 0. 25 mm and 0. 07 mm aluminum diaphragms. It is obviously smaller than the relative errors ( 7. 5% and 4. 3% ) of the measurement results of traditional methods. These results indicate that the proposed method improves the estimation accuracy of the rise time of shock tube reflection step pressure effectively and has stronger robustness.

Abstract:In this article, a simultaneous measurement system of space charge and current as well as space charge and the potential in open circuit based on the “ region division but at the same time” measurement strategy is proposed to obtain the dielectric response information comprehensively and to accurately characterize the dielectric properties of solid dielectrics. The simultaneous measurement system consists of the readymade current measurement unit, potential measurement unit and space charge measurement unit. Those measurement units are reasonably combined through a T-filter and an inductance to isolate alternating component to ensure that there is no mutual interference between each unit during the measurement process. In this way, the simultaneous measurement of the current, space charge and the potential in open circuit could be achieved. The measurement system is validated by the measurement results of the current, potential, and space charge of low-density polyethylene (LDPE) samples with silicon carbide (SiC) filler. Results show that the basic noise of current measurement is quantitative in pA, the voltage measurement is good in the range of 1 ~ 10 kV, and the accuracy of space charge measurement is not affected. The proposed simultaneous measurement system is simple and easy to achieve technically, which can realize simultaneous measurement of current, potential, and space charges of solid dielectrics. In addition, the combined simultaneous measurement system still has room for further expansion and provides reference and basis for combined simultaneous application of multiple measurement technologies.

Abstract:Based on the current settlement monitoring requirements of high energy photon source (HEPS), the research group designs the key parameters and structure, and develops the first micron monopolar plate capacitive hydrostatic level sensor prototype in China. This article focuses on the calibration and accuracy verification of the level sensor. To address the problems of low calibration accuracy and high machining requirements of the traditional standard block and translation stages calibration method, the single level sensor direct calibration method and multi-segment fitting parameter acquisition scheme are innovatively adopted to realize the micron calibration within 10 mm range. Meanwhile, the laser feedback interferometer combines with nano translation stages is proposed to directly monitor the change of liquid level to realize the full range accuracy verification of single level sensor. And the accuracy is 3 μm. The accuracy of the two-point monitoring system with 7 m distance is evaluated by the heavy-duty translation stages. The relative monitoring accuracy of height can reach 5 μm in the range of 10mm. Results show that the accuracy of the prototype meets the design requirements, which can provide a reliable means for high-precision height monitoring of large-scale devices in China.

Abstract:In this article, a two-stage ejector for the subzero refrigeration system of fishing vessels is proposed, and the optimization design and performance analysis are studied. A two-stage ejector optimization design method based on the flow field parameter matching is proposed to achieve maximum entrainment performance. The computational fluid dynamics (CFD) method is adopted for the ejector modeling and parameter optimization. In addition, the water-cooled condenser is employed to reduce the pressure difference between condenser and evaporator. In this way, the higher entrainment ratio can be achieved. Simulation results show that the proposed two-stage ejector can achieve evaporating temperature as low as 248. 15 K and the optimal area ratios of first and second stage ejector are 11. 8 and 6. 5, respectively. As the intermediate pressure increased from 253 to 324 kPa, the entrainment ratio rises firstly and then decreases. It reaches a maximum value of 0. 096 when the intermediate pressure is 304 kPa, with the coefficient of performance reaching 0. 074. It can conclude that it is energy efficient to utilize the waste heat from fishing vessels to drive the subzero refrigeration system with the optimal designed two-stage ejector and a water-cooled condenser.

Abstract:Compared with calibration, the self-calibration does not require a standard artifact with higher precision level than that of the object to be calibrated, which can achieve the calibration of system errors of precision measuring instruments at a low cost of conditions. The standard artifact in 2D self-calibration usually employs traditional three-positions combination of initial position, relative rotation and translation. The positions are independent of each other, and all position transformations are based on the initial position. The utilization of the hybrid position combined with rotation and translation can void the redundant operation of returning the standard artifact to initial position between two position transformations to simplify the process of 2D self-calibration. The 2D self-calibration based on hybrid position is proved by simulation experiments to be effective in separating the errors of the calibrated objects in the simulated noisy environment, and its uncertainty is equal to that of the traditional combination of non-hybrid positions. The introduction of hybrid position has no influence on the noise suppression capability of 2D self-calibration. The 2D self-calibration experiments are conducted on a vision measuring machine, and the stage system errors separated by the 2D self-calibration using a three-positions combination including a hybrid position are close to that of traditional three-positions combination. The results of the stage system errors corresponding the two positions combinations differ by 0. 167 μm. In addition, the effect of two-dimensional self-calibration based on hybrid position is further demonstrated by experimental results using a four-positions combination including a hybrid position

Abstract:In this article, a dynamic clarance measurement model based on the swept frequency interferometry is formulated. The relationship among the clarance, Doppler error and light source sweeping speed is deduced. The Doppler error correction coefficient is defined to compensate for the Doppler error. The characteristics of the instantaneous frequency change of the clarance are analyzed when the sweeping speed is changed. And the applicable conditions of the Fourier transform demodulation method are obtained. The influence of the number of sparse frequency sweeping points and noise on Fourier transform demodulation is simulated and analyzed when the clarance changes in the form of a triangular wave. Then, the applicability of the method in Clearance of labyrinth measurement is analyzed. Finally, a clearance measurement system based on a fast sparse frequency-swept Clearance of labyrinth interferometry is setup and dynamic measurement experiments are carried out. Results show that the mean square error of measurement is less than 0. 2 μm when the sweeping rate is 20, 40, and 60 kHz, respectively. The clarance change frequency is 100, 500, and 1 000 Hz, respectively. And the repeatability error is less than 0. 3 μm.

Abstract:The existing low-temperature plasma technology has limitations of small discharge range and high discharge voltage, which is not suitable for the disinfection and sterilization of large-sized medical devices. In this article, a sterilization device is developed that generates large-area plasma at lower breakdown voltage values (atmospheric helium 325 V, and atmospheric air 585 V) and innovatively achieves gas discharge on the printed circuit board. Through the plasma simulation in COMSOL software and actual discharge experiment, the uniform glow discharge is achieved under both low and atmospheric pressure environment. The discharge experiment results show that the breakdown voltage is the lowest when the relative air pressure is in the range of - 70 to - 50 kPa. Through sterilization experiments, the sterilization efficiency of the generated plasma is verified, and the factors affecting the sterilization effect are explored. Results of the sterilization experiment show that the frequency of the power supply is the most critical factor affecting the sterilization effect, following by the sterilization time, which the helium pressure has minimal effect on sterilization. The complete sterilization can be achieved in 6 min for a certain concentration of Escherichia coli.

Abstract:To meet the requirements of field attitude measurement accuracy evaluation of large-scale precision engineering, a method of attitude angle field accuracy evaluation is proposed by tracing the measurement results of attitude angle to the length measuring standards. Firstly, the basic composition and measurement principle of the laser tracking attitude measurement system are introduced. Secondly, the mathematical model between space distance and target attitude is formulated, which is based on the forward kinematics research of six-degree-of-freedom (6-DOF) parallel mechanism. The Monte Carlo method is used to simulate and analyze the effect of distance constraint measurement accuracy, control field layout and the working distance of the system on the evaluation model accuracy. Finally, an experimental platform is established, and the relative rotation of the precision turntable is used as the angle benchmark to evaluate the feasibility of the research method. Results show that the azimuth accuracy of the model is 0. 055 ° and the pitch accuracy is 0. 058 ° within the angle range of - 20° ~ 20°, when the distance constraint measurement accuracy is 0. 038 mm and the size of the control field is 1 400 mm×1 400 mm. In conclusion, the research method in this article avoids the strict requirements of the coordinate system registration in the evaluation method based on angle benchmark. It could comprehensively reflect the field use state of the measurement system, which can provide a reference for the attitude angle field accuracy evaluation method in the 6-DOF laser tracking measurement system.

Abstract:As the number of spacecrafts increasing, it is particularly important to diagnose the fault of spacecraft tracking telemetry and control (TT&C) system quickly and accurately. To address the problems of large changes in the space environment, complex telemetry data components and low accuracy of fault diagnosis, a fault diagnosis method of spacecraft TT&C system based on the attention mechanism residual network (AM-ResNet) is proposed. Firstly, the telemetry data are converted into grayscale image. Secondly, the image is passed through the residual network (ResNet) and attention module to obtain feature map with global dependence. Finally, the softmax classifier is used to achieve image classification after convolution and pooling operations to realize the fault diagnosis of spacecraft TT&C system. Experimental results show that the fault diagnosis method of spacecraft TT&C system based on the proposed AM-ResNet can improve the accuracy of fault diagnosis to be 95. 68% . Compared with ResNet-18, AlexNet and LeNet-5 fault diagnosis models, the diagnostic accuracy is increased by 3. 53% , 5. 62% and 16. 43% , respectively, which prove that the method can effectively improve the fault diagnosis performance of the spacecraft TT & C system.

Abstract:The field data of wind turbine data acquisition and supervisory control system are commonly missing, which have a certain negative influence on the downstream condition monitoring task. To address this issue, a mask autoencoder network with attention mechanism is proposed to impute missing values in panel data samples, increase the number of available samples, and improve the accuracy and continuity of condition monitoring results. The method takes the denoising autoencoder network as the overall framework. In the encoding stage, the missing values are masked by the attention mechanism, and the missing values are given a higher weight to strengthen the attention of the network. In the decoding stage, the complete data samples are output after missing values imputation. Then, the parameter of the target variable is predicted by using the sample features extracted by long short-term memory network, and the condition monitoring is realized according to the prediction residual. This method is evaluated by the operation data of a wind turbine gearbox. Results show that the data imputation bias of the proposed method is at least 17. 2% better than that of the comparison method. Compared with before data imputation, the number of samples increased significantly after data imputation, which makes the prediction residual of normal data decreased by 37. 4% on average and the detection rate of fault data increased by 6. 8% .

Abstract:The data-driven deep learning methods have achieved excellent performance in the mechanical fault diagnosis of high-voltage circuit breakers. However, the premise of these methods to achieve excellent performance is the ability to obtain massive training samples. The diagnostic performance is severely degraded in scenarios where field data are scarce. To address this issue, this article proposes a novel feature fusion metric learning model for mechanical fault diagnosis of field high-voltage circuit breakers. First, a feature fusion convolutional neural network is established, which effectively improves the ability to extract discriminative features. Then, the K-nearest neighbor algorithm is used as the metric learner to realize the matching and classification of few-shot. Finally, the discriminative ability of feature representation is further improved by improving the center loss. And the transferable knowledge is learned from the large sample set constructed in the laboratory through episodic training. Experimental results show that the proposed method can achieve the diagnosis accuracy of 94. 58% when the number of samples in each type of support set is 5. It is 63. 71% higher than that of the convolutional neural network. In addition, the proposed method benefits from the episodic training method, which effectively avoids the problem of unbalanced samples.

Abstract:To reduce the transmission risk of high-power laser in a single fiber, a 19×1 incoherent space laser beam combiner with circular spot based on multiple optical fiber transmission was presented. The coaxial close arrangement based on “ inner and outer concentric circles + center” is applied in the optical design of the combiner, and the lenses parameters of the combiner are obtained by ray tracing method. Simultaneously, to evaluate the long-term operation reliability of the 19 × 1 laser beam combiner accurately, the coupled thermo-mechanical characteristics of all optics in the beam combiner is analyzed, to clarify the distributions and evolution laws of temperature field and thermal stress field of optics by using the finite element method ( FEM). The multi-beam laser volumetric heat source model is established based on the overall heat source distribution of 19 laser beams synchronously transmitted optical lenses. Then, the temperature field, thermal displacement and stress of optics are simulated. Results show under the irradiation of 10 kW laser for 30 minutes, the temperature of all optical lenses in the beam combiner with the maximum of 381. 11 K is far lower than the softening temperature of 1 900 K of the fused silica material. The thermal deformation with the maximum corresponding aperture number of 0. 07 is within the design tolerance, and stress with the maximum of 14. 02 MPa is less than the yield stress of 4. 5 GPa of the fused silica. In addition, the total laser power output by the beam combiner can reach 10. 43 kW . The study provides an effective method reference for evaluating the long-term operation reliability of other high-power laser source.

Abstract:The traditional star points position error testing method cannot be applied to infrared star simulators. To address this issue, a new method for star points position analysis, simulation and test of infrared star simulators is proposed. Combined with the traditional star points position testing method of star simulator, the influence of aberration on star points position simulators is analyzed. The theoretical model of aberration and star points position simulation is formulated. Through error fitting the theoretical relationship that good image quality represents small single star points position error is analyzed. According to the docking mode of star simulator and star sensor, a visible and infrared light dual configuration collimating optical system with large exit pupil distance and an external position is designed, which could make the image quality of infrared band better than that of visible band. The accuracy of the theoretical model is evaluated by theodolite measurement. The actual measurement results show that the single star position error in visible band is better than ±13. 65″. It indirectly realizes the single star position error in infrared band better than ±13. 65″

Abstract:The model recognition accuracy is low due to the scarcity of fault sample data in practical engineering. To address this issue, a rolling bearing fault diagnosis model based on the self-calibrated convolutional neural network ( SC-CNN) is proposed and applied to fault identification under the condition of small samples. Firstly, the BN algorithm is added after each convolutional layer to reduce the data distribution difference of different signals. Secondly, the self-calibrated convolution is adopted to learn the multi-scale features of the signal to improve the ability of the model to obtain useful fault features. Then, the channel self-attention mechanism is introduced to establish the correlation between channel feature information to highlight the fault features and suppress data overfitting. Further, a small number of training samples are fed into the model for learning. Finally, the fault signals under various conditions are taken as the input of the trained SC-CNN model for identification and classification. Evaluation experiments are implemented on two datasets. Results show that the recognition accuracy values of the proposed model are 98. 64% and 99. 83% under strong noise environment with SNR of -4 dB. Those two values are 94. 37% and 99. 64% under variable working conditions. Results show that the SC-CNN model has strong robustness and generalization performance under small sample condition.

Abstract:The improvement of reliability is a key problem to be solved in the research and development of the domestic control system of the heavy-duty gas turbine. Hardware redundancy is an important guarantee for enhancing reliability of the control system, which can realize fault tolerance in function. Its cost is lower than repair. It has occupied a dominant position in industrial application. However, the development of domestic control system of the heavy-duty gas turbine is still at the early stage. Hence, the research on its reliability is almost blank. This article analyzes redundancy strategy of the domestic NuCON control system of the heavy-duty gas turbine, which uses Markov model accordingly to formulate the reliability model. The probabilistic model checker PRISM is utilized to calculate and test the reliability model quantitatively. The simulation shows that the unavailability of minimum hardware configuration of the NuCON control system is 1. 616×10 -4 , which meets the design requirement of system reliability. Meanwhile, the quantitative effects of different repair time, different system structure and different system scale on the unavailability of the system are compared and analyzed.

Abstract:Elasto-magnetic(EM) tension sensing technology has many advantages in the tensile monitoring of flexible cable. However, the problem excites that the component under test cannot be fully magnetized by the excitation coils when this method is used to the solid round steel suspender. Herein, we analyze the magnetization process of the round steel hanger, according to its structure and mechanical characteristics. Then, the excitation coils of the sensors are optimized by modeling and simulation. Two kinds of sensors are developed for the conventional round steel suspender with a diameter of Φ85 mm, and a larger one with a diameter of Φ120 mm. The performance of two sensors are tested thought the static loading experiments. And the conventional strain gauges are used for comparation. Experimental results show that the optimized excitation coil can fully magnetize the Φ85 mm round steel suspender. The full-scale error of EM-sensors is less than 2% (average value -0. 15% ), which is significantly better than the result of the strain gauge (average value -1. 95% ). Experimental results demonstrate the superiors of EM sensors in linearity and repeatability, which meets requirements of the tension measurement of the conventional diameter solid round steel. The excitation coil for Φ120 mm round steel suspender should be further optimized for the guarantee of the accuracy and robustness of the sensor.

Abstract:Due to the advantages of high sensitivity, reliable performance and high measurement accuracy, the thermal microflow sensors are widely used in various fields, such as environment monitoring systems, biological research, process control chemical engineering, and so on. However, the limited range of such sensor impedes the extension of application fields. In this article, a new double-layer and two-channel thermal microflow sensor is proposed for wider measure range. The temperature-difference output is from the sensor in mainchannel at low flowrate, from that in side-channel at high flowrate. To improve the performance of microflow sensor, simulations are conducted to obtain the preferential parameters, such as the size and position of inlet for side-channel, as well as the height and width of main-channel and side-channel. The measure range of improved microflow sensor is 0~ 145 m/ s when the sensitivity being greater than 0. 03 K/ (m·s -1 ). The switching threshold of inlet velocity for temperature-difference output from the main-channel to side-channel is 63 m/ s. Finally, the viscous dissipation of in the microflow sensor is analyzed and found, the viscous dissipation at the front-end of the upstream detector is the main cause of the failure of microflow sensor. This can provide an effective method for improving the performance of the thermal microflow sensor.

Abstract:The installation pose error of the laser probe mounted on a three-axis CNC machine tool is not easy to adjust, which may result in measurement error. In this article, a posture calibration method of the laser probe for on-machine measurement (OMM) is proposed. A mathematical model based on the laser line profiler for OMM is formulated, the spatial pose of the calibration reference point is measured by the laser probe, and the linear algorithm to obtain the pose parameters of the probe installation is derived based on the handeye calibration. Considering the influence of the positioning errors of the machine tool on the accuracy of the calibration, an error analysis is implemented by using Monte Carlo simulation. Experimental results show that when measuring a circular hole with a radius of 35 mm, the measurement error of the circular hole after calibration is 0. 051 6 mm, and the measurement accuracy is improved by approximately 96% , which validate the effectiveness and feasibility of the proposed calibration method.

Abstract:Due to the limitations of the size, weight and power of micro-nano satellites, the field programmable gate array (FPGA) is required to provide high energy efficiency computing ability for the onboard sea-land segmentation of large filed remote sensing images. Under the premise of ensuring the segmentation accuracy, the key is to utilize the limited on-chip resources of FPGA to realize parallel computing for low computational complexity algorithms. Therefore, a multi-threshold hierarchical sea-land segmentation based on FPGA parallel computing is proposed in this article. The proposed method takes OTSU as the core method, and uses the sub-image classification based on multi-feature joint threshold to construct hierarchical sea-land segmentation. In this way, the influence of interference points in the sea are suppressed. In addition, a novel parallel iterative computing architecture is designed, which can improve the computing efficiency of OTSU, and achieve the balance adjustment of on-chip memory occupancy. Experimental results show that the overall precision of the method proposed can achieve more than 98% . Meanwhile, the processing time of the proposed method is only 0. 16 s for the 8 192×2 048 pixels remote sensing image. Compared with other FPGA-based methods, the processing time is reduced by 23. 81% at least.

Abstract:Piezoelectric vibration energy harvester is the important component of wireless self-power supply for low-power electronic products, and its coupling mechanism with nonlinear power extraction circuit is the key theory to improve the output performance of the wireless self-power supply system. Taking the nonlinear tristable piezoelectric vibration energy harvester and four different power extraction circuits as the object, the coupling dynamic model of the presented system is firstly formulated, and the steady state solutions of the coupled dynamic response of the system under different interface circuits are obtained by using the harmonic balance method. The effects of system parameters on the output characteristics of the interface circuit are simulated and analyzed. Results show that when the electromechanical coupling coefficient is too small, the S-SSHI circuit is suitable for occasions with frequency less than 7 Hz and load resistance less than 7. 4 × 10 6 Ω, while P-SSHI circuit is opposite. The DC circuit has advantages when electromechanical coupling coefficient is too large, and the output power is 4. 5×10 -3 mW. AC and DC circuits have a wide range of electromechanical coupling coefficients, while P-SSHI and S-SSHI circuits have a narrow range but have high output power, and the maximum output power can reach 19. 0×10 -3 and 14. 3×10 -3 mW. The simulation results are verified by experiments.

Abstract:The efficient separation of coal and gangue is an important way to realize green mining of coal resources, and the core technology is the rapid and accurate identification of coal and gangue. Therefore, a coal-gangue recognition method based on the fusion of X-ray image and laser point cloud is proposed in this article. Firstly, an improved Otsu segmentation algorithm based on the local entropy and global mean difference weighting is designed to enhance the segmentation accuracy and efficiency of X-ray images. Meanwhile, the straight-through filtering and voxel grid down sampling are used to simplify the laser point cloud data of coal and gangue, and the coalgangue feature combination of X-ray image and laser point cloud is extracted. Then, to address the problems that the traditional sparrow search algorithm (SSA) is prone to fall into local optimum and the population diversity is poor, a multi-strategy improved SSA algorithm (ISSA) is proposed to optimize the model parameters of light gradient boosting machine (LightGBM). A coal-gangue fast recognition model based on ISSA-LightGBM is designed. Finally, an experimental platform for the coal-gangue recognition is established and the corresponding experimental comparative analysis is carried out. Results show that the comprehensive recognition accuracy of ISSALightGBM model can reach to 99. 00% , and the comprehensive performance is superior to other models, which could meet the needs of efficient coal-gangue recognition.

Abstract:The railway obstacles in front of the train have great threat to traffic safety. The existing railway object detection algorithms are difficult to balance the detection accuracy and speed, which are susceptible to complex environment and difficult to deploy in embedded equipment. To address these issues, the lightweight and adaptive multiscale convolutional neural network is proposed in this article. The model simplifies the computation of redundant feature maps in feature extraction process by means of feature map linear transformation, and the adaptive multi-scale feature fusion is used to optimize the ability and further improve the accuracy of foreign obstacles detection. In addition, combined with NVIDIA Jetson TX2, an autonomous intrusion detection system for railway traffic scenes is developed. Experimental results show that the proposed model performs a great compromise between detection speed and accuracy. The detection speed of LAM-NET on the NVIDIA GeForce GTX1080Ti is 297 FPS, and the detection accuracy is 92. 96% (7. 72% higher than that of YOLOv4-tiny), which can well realize the high precision, real-time and high robustness detection for railway obstacles.

Abstract:A vision-based measurement ( VBM) system composed of two oppositely placed VBM units is designed to rapidly and accurately measure the coaxiality error of large-scale parts with long-span holes. The VBM unit is a line laser scanning stereo vision system, and two VBM units are used to obtain the point clouds of the end faces of the two holes. The two-point clouds are then used to calculate the centers of the circles and the normal directions on the end faces, respectively, as well as the two hole axes. The calibration method, using a specially designed target made up of four fixedly connected calibration boards whose position and orientation relations are accurately calibrated beforehand, is proposed to realize the unification of the coordinate systems of the two VBM units. These two independent axes are then merged into a common coordinate system. Finally, the coaxiality evaluation parameters are given. The accuracy verification experiment shows that the RSME of the center distance of the standard ball set with 1. 15 m span is 0. 161 mm. The proposed system is applied to measure the coaxiality of the bearing holes on both sides of the vibrating wheel of a roller, and the results are compared with those of the CMM. Results show that the deviation of the two measurement results is less than 5% , and the measurement accuracy can meet the needs of industrial production.

Abstract:Three dimensional medical images can help doctors in diagnosis and treatment, and the registered medical images of different modalities can provide more comprehensive information about the patient for doctors. However, the traditional 3D multimodal medical image registration is less precise, time-consuming and susceptible to interference. This article first establishes the Hessian fourdimensional scale space, and extends the SURF framework to 3D. Then, a 3D feature point descriptor with gradient angle invariance is constructed based on geometric algebra to enrich feature point information. Meanwhile, a fast spatial optimization algorithm is designed, which can not only ensure the registration accuracy, but also improve the registration stability. Finally, the experiments are carried out using the RIRE public data set with good data consistency and the personalized clinical instance data provided by the cooperative affiliated hospitals. In the experimental evaluation, with manual registration as the gold standard, the average registration error of the public library and clinical example images does not exceed 3 mm, and the registration similarity exceeds 99. 1% . Gaussian noise is mixed in the anti-interference experiment, the mean error still does not exceed 3. 5 mm, and the similarity exceeds 98. 9% . Experimental results show that the 3D registration method based on geometric algebra SURF has higher accuracy and stability, which can provide theoretical basis and treatment plan for clinical application.

Abstract:When conventional algorithms are used to solve the survey task planning problem of unmanned underwater vehicle (UUV) swarm under limited endurance and load constraints in a wide and sparsely distributed area, the poor convergence and low solution quality are common problems. In this article, an improved ant colony optimization algorithm is proposed. Firstly, by analyzing the constraints of individual UUV platform capability and swarm task, the constraint model and optimization model of UUV swarm task planning are formulated. Secondly, a method of unequal allocation of initial pheromone concentration is designed based on the difference between the average distance and the distance between task points, the optimal and worst thresholds of the optimization model are proposed to classify the ants and complete pheromone update, introduce an innovative “ gravity factor” that dynamically changes according to the iteration process to the state transition rule to increase the probability of the suboptimal node being selected in the early and middle of the algorithm. Thirdly, the validity of the algorithm optimization item is analyzed by statistical experiment and the algorithm parameters are optimized according to the number of optimal solutions and average convergence value. Finally, based on the cases from classic documents, by making comparison analysis with basic ant colony algorithm and elite ant colony algorithm, the proposed algorithm in this article improves the percentage of finding approximate optimal solution by 78% and 66% in 50 statistical experiments and average convergence in the 40th generation, which shows good global optimization capability and convergence performance. Experimental results of a designed typical UUV swarm mission planning case with a certain scale show the rapidity and effectiveness of this algorithm in solving the problem of the swarm survey task planning in a wide and sparsely distributed area.

Abstract:The existing wireless energy transfer systems are suitable for gastrointestinal robot that have low energy transmission efficiency, low received power, and poor location uniformity of received energy, which cannot meet the energy requirement of new gastrointestinal robots with complex functions. In this article, a new type of C-shaped combined transmitting coil structure is proposed. The magnetic flux density of the power supply unit is analyzed by finite element simulation, and position uniformity, determine the experimental optimization range of the coil pair spacing in the power supply unit group. Finally, an experimental platform is established to optimize the coil pair spacing distance in the power supply unit, and the design is evaluated and verified through the energy transmission efficiency of the system, the received power, and the position uniformity of the received power. Experimental results show that when the coil pair spacing distance is 150 mm, the received power at the center position is 1 165. 34 mW, and the energy transmission efficiency of the system is 6. 08% . The average power of the system is about 1 100 mW, the average energy transmission efficiency is over 6% , and the average position uniformity of the received power is 94% . By introducing a new type of C-structure magnetic core and adopting a combined coil structure in which only one power supply unit group works at the same time, the received power and energy transmission efficiency of the system are greatly improved, and the position uniformity of received energy is high.

Abstract:The internal torque of fingers is affected by surface electromyography ( sEMG) signals, muscle strength, hand posture and other factors, which cannot be obtained directly. To obtain the torques and coupling torque of finger joints in real time as well as accurately, and apply them to the interactive control of hand rehabilitation robots, a method for analyzing and acquiring the torques and coupling torque of finger joints based on sEMG and musculoskeletal model is proposed. Firstly, an adaptive finger joints angle acquisition system is designed. Through experiments, the sEMG signals of the flexor digitorum superficialis ( FDS) and the extensor digitorum communis (EDC) and the angle data of each finger joint are collected simultaneously. The torque model of the finger joints is formulated and the torque of each joint of the fingers is obtained. Then, the finger D-H model is established, and the coupling torque of the finger joints is obtained by combining the principle of virtual work. Finally, the parameters of the multi-joint torque model are determined, and the simulated torque is achieved through OpenSim software. By comparing the results of the calculated torques and the simulated torques, the root mean square error of three joint torques of the four subjects are 0. 156 7, 0. 097 425 and 0. 084 95, respectively. Results show that the method can obtain the torque and coupling torque of each joint of the finger in real time and accurately, which can meet the requirements of accuracy and real-time interactive control of hand rehabilitation robots.

Abstract:To solve the problems of unstable and low reliability calibration results due to the random selection of calibrated pose points, the number of optimal pose point and the selection algorithm of optimal pose set based on the singular value of Jacobian matrix to calculate observable indexes are studied. The MDH model is formulated and the Levenberg-Marquardt (LM) algorithm is used to identify geometric parameters. The points of the Staubli TX60 robot end-effector selected based on the optimal and random pose set are measured by the LeicaAT960 laser tracker. On the basis of analyzing and studying the uncertainty contributors of robot calibration, the GUM method is used to calculate the uncertainty of geometric parameter calibration and Monte Carlo simulation method is utilized to evaluate the uncertainty of robot end-effector pose, respectively. Results show that the accuracy of the robot calibrated by the optimal pose set is not only greatly improved at the test points, but also the mean uncertainty of geometric parameters and end-effector is about 0. 11 times of that calibrated by the random pose set. The calibration results are stable and reliable, and the generalization ability is strong, which are suitable for popularization and application in high-precision and large-scale operation situations.

Abstract:Considering an unstable and under-actuated two-degree-freedom rotary inverted pendulum system, this article proposes a double closed-loop disturbance rejection PID control method based on differential flatness theory. Firstly, a nonlinear dynamic model is formulated for the rotating inverted pendulum. The unstable zero-dynamics and non-minimum phase properties have been analyzed by the approximate linearization method. Then, the differential flatness theory is used to design flat output and derive state reconstruction. The relationship between the flat state and the angle out is established. In this way, the non-minimum phase can be removed by flatness transformation. The disturbance rejection PID control is developed for the designed flatness system with a dual closed-loop and the bandwidth tuning method is also derived. The two angles of the rotary inverted pendulum are controlled precisely based on active disturbance rejection mechanism. Finally, simulation and experiment are implemented to illustrate the effectiveness of the proposed method, which provides a simple and robust control scheme for underactuated systems.