Abstract:Particle emissions from diesel engines are the primary contributor to the ultra-fine particle in the urban atmosphere, and have severely reverse effects on environmental quality and human health. In recent years, particle emission is strictly regulated in the emission legislations, which requires the higher performance of instruments for particle number measurement. In this article, five particle number measuring instruments are summarized in their measuring principles and features. Then, the performances of those instruments under specific requirements and operating conditions are compared. Finally, the accessibility of those instruments is analyzed for the measurement requirements of the upcoming emission regulations. The result shows that the condensation particle counter (CPC) has a faster response to detection of >10 nm particle number emissions, which can meet the test requirements of the urban RDE test cycle and 10 ~ 23 nm in future regulations. However, CPC cannot measure the particle size distribution, so it is not suitable for the development of controlling technology for 10~ 23 nm particles. The engine exhaust particle sizer (EEPS) can give a size distribution of as small as 5. 6 nm particles, which may provide complementary information for the CPC detection for the upcoming emission legislations.

Abstract:To solve the practical problems such as poor long-term stability and large zero drift of clock-type vector magnetometer, mutual interference and high component noise of integrated suspended spherical coils, a proton vector magnetometer based on split-suspension spherical coil is proposed. The measuring principle and instrument structure are emphatically introduced. In addition, a new type of lowcost simple observation chamber is designed, and an one-year comparative observation experiment is implemented at Mengcheng seismic station using the two successfully developed prototypes. Results show that, total field noise, horizontal component noise and magnetic declination noise of prototype Ⅰ are 0. 23 nT, 0. 24 nT, and 2. 97″, and those values of prototype Ⅱ is 0. 28 nT, 0. 26 nT, and 3. 29″, which are lower than the average noise level of national network. The total field noise and horizontal component noise of prototype Ⅰ are better than the lowest noise of the network. The maximum horizontal components baseline deviation of two prototypes in a year is 6. 5 nT and 4. 7 nT, respectively. The maximum baseline deviation values of magnetic declination are 0. 67′ and 0. 54′, which are obviously lower than the baseline deviation average level of clock-type vector magnetometer of the network. The proton vector magnetometer based on split-suspension spherical coil has great stability.

Abstract:In this article, the Monte Carlo method is used to evaluate the uncertainty of the robotic arm calibration system. First, this method formulates a model for each variable of the manipulator calibration system. Then, the actual distribution of the input is obtained by establishing the actual data set as the input of the manipulator calibration system. To evaluate the correctness of this method, the main sources of uncertainty in the calibration process are analyzed, and a method based on Monte Carlo uncertainty evaluation is proposed to evaluate the calibration process of the manipulator based on regularization. By using laser tracker as the measurement tool, the method of robot regularization parameter identification is evaluated experimentally. And the uncertainty evaluation of robot calibration is carried out. The results show that the relative expanded uncertainty of each axis is better than 0. 542 2% , 1. 325 9% , and 0. 015 4% , respectively.

Abstract:It is difficult for conventional instrument to measure the thermal expansion coefficient and shrinkage rate of thermosetting resin because of the strong coupling among cure degree, thermal expansion and chemical shrinkage volume during cure process. To solve this problem, a measuring device is designed with reference to the PVT-α method. The reaction heat and cure temperature are obtained through the heat flow sensor and thermocouple of the device. Then, the cure degree of thermosetting resin sample can be determined by the integral of reaction heat. The volume change of thermosetting resin sample is determined by the displacement sensor. Combining the volume change with the evolution of cure degree, the thermal expansion and cure shrinkage volume during cure process are decoupled to obtain the thermal expansion coefficient and the shrinkage rate at the same time. By using this device, the thermal expansion coefficient of silicone rubber material is measured. Compared with the standard value, the error is 4. 93% , which verifies the feasibility of the device. The thermal expansion coefficient of thermosetting resin (epoxy resin E-51) during cure is measured, which decreases with the cure degree linearly, with the expression: CTEcross( α) = 8. 265 1× 10 -4 ( 1-α) + 7. 566 4 × 10 -4 α ( 1 / ℃ ), and the shrinkage rate is determined at the same time, with the value of 1. 87% . The developed device in this article provides a method for measuring the thermal expansion coefficient and shrinkage rate of thermosetting resin during cure.

Abstract:The linear structure light sensor is used to measure the three-dimensional error of the gear, which has advantages of fast, full information, and high repeatability. However, the position and posture relationship between the sensor and the gear under test is the key issue that has influence on the accuracy of gear measurement. To optimize the position and posture relationship between the probe and the gear, the quantitative evaluation index of the position and posture of the sensor is proposed. To evaluate the effectiveness and correctness of the proposed method, measurement experiments of the same gear product of different posture parameters are carried out, and compared and analyzed with the Klingberg P26 gear measurement center. Compared with the conventional measurement method, results show that the point cloud density ratio Λp of the optimization method is 33. 6% higher than the index, and the inclination ratio υp is 30. 2% higher than the index. The measured tooth profile unit is more complete and more in line with the data requirements of the international standard ISO1328. The individual gear error evaluation results are also more accurate. By using this method, parameters of the position and posture of the linear structured light sensor are estimated, which are used for gear measurement. Thereby, the accuracy of gear measurement is improved. It provides a way to solve the problem of non-contact measurement of steep tooth surfaces.

Abstract:The optical system characteristics and working index requirements of a certain type of space tracking remote sensing camera are considered. To save space and ensure the image quality of the camera, a two-dimensional scanning mirror suitable for small satellite platform is designed, and the light weight of the scanning mirror for the 300 mm space camera is realized. The thickness of mirror body, the cutting angle, the thickness of edge, the thickness of mirror surface and the thickness of lightweight reinforcement are taken as the design variables. The target value of peak PV is smaller than 63 nm and that of RMS is smaller than 12 nm. These two values are taken as the optimal boundary condition. A multi-objective optimization method is proposed to minimize the mass, surface peak PV and mean root RMS under certain equilibrium for the parameter design of single point support aperture. The weight of the optimized mirror is only 0. 84 kg, and the lightweight ratio reaches 76% . The single point flexible support structure uses three loop beams spaced 120° apart as the flexible hinge, and utilizes the integrated design with the mirror seat. The simple structure reduces the moment of inertia of the scanning mirror component and improves the response speed of the tracking camera. The space environment of the optimized scanning mirror component is checked. The RMS value of the surface shape of the scanning mirror is less than 6nm in XY microgravity; the RMS value of the surface shape of the scanning mirror is less than 12 nm in Z gravity, -50℃ load and 3. 5 rad / s rotational inertia load; the fundamental frequency of the component is 326 Hz. Finally, the experiment evaluation of the surface shape accuracy and the positioning accuracy of the scanning mirror proves the feasibility of the scheme.

Abstract:Aiming at the requirement of precise attitude measurement in the fields of aerospace, automobile and ship, and robot application, a laser tracking attitude angle measurement method based on visual weighted accelerated orthogonal iteration ( WAOI) is studied. Firstly, the composition of system is described, the mathematical measurement model is established, and the main error sources of system are analyzed. Secondly, on the basis of orthogonal iteration (OI), the weight coefficient of reference point was set by the object square reprojection error, and a WAOI algorithm was proposed to integrate the redundant calculation in the iterative process by introducing constant coefficient matrix, and the performance of the algorithm was verified by experiment. Finally, an experimental platform is built, and a precision two-dimensional turntable is used to evaluate the accuracy of the attitude angle measurement based on WAOI. The results show that within the angle range of -20° ~ 20° and within the measurement range of 3~ 15 m, the azimuth accuracy can reach 0. 11° and the pitching accuracy can reach 0. 26°. Compared with the proportional orthogonal projection iterative change (POSIT), the accuracy of azimuth and pitch angle is improved by more than 75% . The WAOI algorithm proposed in this paper effectively improves the accuracy of laser tracking attitude measurement system.

Abstract:The thermal positioning error of linear axis is related to position and temperature at the same time, and the traditional modelling method has a heavy workload, low efficiency and poor prediction accuracy under variable condition. To address this issue, a decoupling and step-by-step modelling method for thermal positioning error of linear axis is proposed in this article. Firstly, the thermal positioning error measured is decoupled, and the slope parameters and intercept parameters only related to the temperature are obtained based on least square linear fitting. Secondly, the absolute temperature and relative temperature are used to build the slope parameter model and intercept parameter model step by step, and the mapping relationship is obtained. Combined with the slope and intercept, the thermal positioning error model is formulated. Finally, based on the established model, the thermal positioning error in a new working condition is predicted, and the maximum residual error can be realized as 1. 6 μm. Compared with the direct modelling method, the prediction accuracy of the proposed method is improved greatly, which shows its effectiveness.

Abstract:A multi-beam interference optical fiber Fabry-Perot probe for pulsating micro-pressure sensing is proposed in this article. The probe′s multi-beam interference wavelength drift and micro-pressure sensing models are formulated, and the micro-pressure sensitivity difference between gas and liquid environments is analyzed. The optical fiber probes are fabricated by chemical corrosion, electric discharge welding and precision cutting technology. The micro-pressure sensing environment is set up using medical syringe, transparent flexible hose, and quartz ferrules. The internal pressure distributions are analyzed by finite element simulation. Three optical fiber FP probes are measured under the gas pressure from 14. 41 to 85. 22 kPa and the liquid pressure from 4. 50 to 26. 02 kPa. Experimental results show that in both gas and liquid environments, the probe wavelength has a red-shift with the pressure increase, and a blue-shift with the pressure decrease. The probe′s average micro-pressure sensitivity in gas environment can reach 8. 210 pm·kPa -1 . In liquid environment, it can reach 66. 720 pm·kPa -1 , which is higher and is consistent with the theoretical prediction. The optical fiber FP probe featuring the highest micro-pressure sensitivity in both gas and liquid environments is selected for the liquid pulsating micropressure sensing measurement. Experimental results show that the probe has good wavelength response and the repeatability error is small within five pulsating cycles. The optical fiber FP probe proposed in this article has the advantages of compact structure, easy fabrication,and high micro-pressure sensitivity. It can realize pulsating micro-pressure sensing in the frequency range of 1 Hz, which provides an important reference value for micro-pressure sensing applications in liquid environment.

Abstract:To solve the contradiction among the sensitivity, the dynamic range and the microwave performance of the on-line microwave power sensor fundamentally, a coupled on-line MEMS microwave power sensor is designed creatively in this article. It makes the extraction and detection of microwave power independent of each other. According to the theoretical analysis model, the relationship of the sensitivity characteristics with the coupling degree is obtained, and the differences are analyzed and compared in microwave characteristics and sensitivity characteristics when the coupling degree are 10% and 20% , respectively. Experimental results show that the reflection loss values of the two kinds of coupled MEMS microwave power sensors are both smaller than -20 dB, indicating that they have good reflection performance. The insertion loss of the two kinds of coupled MEMS microwave power sensors is larger than -1. 5 dB, indicating that they have good transmission performance. The sensitivity of the system with 10% coupling degree is 1. 2 mV/ W@ 9 GHz, 1. 4 mV/ W@ 10 GHz and 0. 8 mV/ W@ 11 GHz. And the sensitivity of the system with 20% coupling degree is 2. 4 mV/ W @ 9 GHz, 2. 4 mV/ W@ 10 GHz and 1. 3 mV/ W@ 11 GHz, and they have good linearity. This article has a certain reference value for MEMS microwave power sensors.

Abstract:The two-dimensional wideband direction-of-arrival ( DOA) estimation plays an important role in achieving the underwater acoustic communication and positioning integration. The two-sided correlation transformation (TCT) algorithm is one of the most popular techniques for high-resolution wideband DOA estimation. However, it is only suitable for one-dimensional DOA estimation under uniform linear arrays, and it has large computational burden. In this article, a fast 2D wideband DOA estimation algorithm with low computational complexity is proposed for arbitrary planar arrays based on the extension and improvement of the simplified TCT (STCT) algorithm. The proposed algorithm performs the matched pre-processing on the array output data by using the known waveform of the underwater acoustic communication synchronization signal, which effectively compresses the number of frequency bins required for the focusing transformation. Therefore, the computational complexity of the focusing transformation is reduced. The accelerated particle swarm optimization (APSO) in cosine domain is used to search for two-dimensional spectral peaks, which significantly reduces the searching complexity while maintaining high searching accuracy. Compared with the pure extended STCT algorithm, the proposed algorithm maintains high DOA estimation accuracy, and the accuracy is about 0. 02° when the signal-to-noise ratio is 20 dB. But, the computational complexity is much lower than the former. Simulation and experiment results evaluate the advantages of the proposed algorithm.

Abstract:The existing radio frequency identification positioning methods have problems of poor positioning accuracy and long positioning time. To address these issues, an RFID indoor multi-target positioning algorithm based on joint range / angle estimation of multi-frequency point phase is proposed. The frequency-hopping technique is employed to obtain multi-frequency phases, which is used to solve the integer ambiguity problem to acquire the rough range estimation of targets. Then, the particle swarm optimization algorithm is used to complete parallel retrieval of the multi-targets location. Meanwhile, the idea of a multiple signal classification algorithm is used to reduce noise and multipath interference to further improve the localization results. Experimental results show that the proposed algorithm can achieve multi-target parallel positioning, and the median error of positioning is 8. 56 cm. Compared with the traditional hyperbolic localization algorithm, the localization time is reduced by 58. 8% .

Abstract:Pressure-sensitive paint ( PSP), as a molecular-level oxygen sensor, has been widely used for high resolution full-field aerodynamics measurement of aircrafts. To apply PSP for the analysis and study of aerodynamic characteristics of aircrafts in near-vacuum environment, PSP with sufficiently high pressure sensitivity at low pressures needs to be prepared and studied to enable small pressure measurements in near-vacuum environments. In this article, two PSPs, namely PdTFPP / PTMSP and PtTFPP / PTMSP, with high-oxygenpermeability polymer PTMSP as the binder, PdTFPP and PtTFPP as luminescent molecules, are used to carry out the static calibration characteristics in near-vacuum environment. Experimental results show that the pressure sensitivity of PdTFPP / PTMSP is significantly higher than that of PtTFPP / PTMSP. While the temperature sensitivity and photostability of PdTFPP / PTMSP are comparable to those of PtTFPP / PTMSP, which is more suitable for measuring small pressure changes in near-vacuum environment. Further characterization of PdTFPP / PTMSP reveals that the pressure sensitivity of PdTFPP / PTMSP increases and then decreases with the increase of the concentration of luminescent molecule and polymer, and reaches the highest value of 68. 65 % / kPa at concentrations of luminescent molecule and polymer are 1. 2 and 8 mg / mL, respectively. The PdTFPP / PTMSP with toluene as solvent has the better pressure sensitivity than the formulation with dichloromethane or acetone as solvent, and has the optimum temperature sensitivity and photostability.

Abstract:Based on the electromagnetic wave propagation theory, this article designs a phase sensor to measure void fraction. By adding a phase mixer to the front of the sensor to convert it into homogeneous flow, the measurement of the void fraction of the slug flow is achieved, and the mixing dielectric constant under different flow conditions is analyzed. The hybrid dielectric constant prediction models, logarithmic, Rayleigh, series-parallel connection, H-B and Bruggenman, are evaluated comparatively with mean absolute percentage error of 41. 51% , 6. 07% , 80. 45% , 62. 51% , and 56. 7% , respectively. A new weighted mixed dielectric constant prediction model is proposed for the slug flow, and the mean absolute percentage error is 4. 37% , with 71. 43% of the data within 5% of the mean relative error. Based on the experimental model of void fraction based on homogeneous flow under the same flow conditions as the reference true value in the experiments of the slug flow model, the results of the void fraction solved by the hybrid dielectric constant prediction model proposed in this article are verified and evaluated. Results show that the mean absolute percentage error of the void fraction prediction model is 0. 34% .

Abstract:To improve the robustness and stability of the robot navigation system in an unknown and complex environment, a Lidar/ MEMS IMU/ Odometer integrated tightly navigation algorithm is proposed. Firstly, the algorithm corrects the distortion point cloud generated by the lidar movement through the pre-integration of the MEMS IMU/ Odometer to improve the feature matching efficiency between two frames of the point cloud. Secondly, the linearly interpolation of the pre-integrated robot posture is implemented according to the timestamp to obtain the rough position change between two frames of the point cloud. This rough pose changing is used as the initial value of the optimization algorithm iteration to reduce the number of iterations of the optimization algorithm. Then, the motion constraint of MEMS IMU/ Odometer is added to the back-end optimization, and the multi-sensor joint optimization is used to improve the positioning accuracy of the robot. Finally, the simulation experiment is carried out using the data set. The indoor and outdoor opening and closing loop experiments are implemented by using the four-wheeled trolley. Experiments show that the average outdoor open-loop positioning error of this algorithm is reduced by 51. 01% and 24. 75% respectively compared with the traditional algorithms ALOAM and LEGOLOAM, respectively, and it can maintain high accuracy when the movement such as cornering is intense.

Abstract:For measuring the explosive radiation electromagnetic pulse (EMP) with fast rise time, narrow pulse width and intense field strength, an integrated optical waveguide electric field sensor is designed and developed. The sensor detected waveform of the applied lightning EMP in the time domain shows almost without distortion in comparison with the original voltage waveform. The minimum and maximum measurable electric fields in time domain are 1. 4 and 10. 8 kV/ m, and the minimum measurable electric fields in the frequency domain is 52. 5 mV/ m. The response fluctuation is within ±4 dB in the frequency range of 9 kHz to 1 GHz. Finally, the insitu testing system of explosive EMP has been setup based on the developed optical waveguide electric field sensor. The radiation EMP at different distance from the explosion place has been measured in the time domain. Results show that the radiation EMP is generated after the explosion for about several milliseconds. The radiation EMP is a series of bipolar pulse train, the width of a single pulse is less than 100 μs, the width of the pulse train is less than 2 ms, and the frequency range is 17. 5 ~ 35 kHz. The EMP field intensity within 5 m from the explosive is about 5 ~ 10 kV/ m. The developed sensor has characteristics of full passive, wide bandwidth, negligible field interference, small volume, and good electromagnetic immunity, which provides a new technique for measurement of the explosive radiation EMP.

Abstract:Based on the Riemann geometric classification algorithm, we explore the possibility of decoding kinematic information of three natural reach-and-execute actions using movement-related cortical potentials ( MRCPs). EEG signals are collected from 9 healthy subjects during the execution of pinch, palmar and precision disk rotation actions that involve two levels of speeds and forces. After preprocessing, MRCPs signals are transformed into covariance space and input into minimum distance to riemannian mean (MDRM) classifier. In this way, we successfully decode the movement parameters of natural hand movements based on MRCPs. For the kinematic parameters of three hand movement, we show that the grand average result of binary classification could reach 89. 24% , and the result of multi-classification could reach 75. 28% . The riemannian framework adopted in this article is novel and efficient, which provides a new way for MRCPs classification of brain-computer interface. Meanwhile, this study is of great importance for controlling neuroprosthesis or other rehabilitation devices in a fine and natural way, which could drastically increase the acceptance of motor impaired users.

Abstract:Functional neural imaging technology can reflect the physiological change of the brain, and decode the movement state. However, the information by the single neural imaging modality is limited. In this article, a time-frequency feature fusion and collaborative classification method is proposed to achieve high precision motion state decoding with EEG and fNIRS signals, which takes the advantage of the complementation of electrical activity and hemoglobin changes. Firstly, the wavelet packet energy entropy feature of the EEG signal is extracted, the Bi-LSTM deep neural network is used to extract the time domain features of the fNIRS signal, and the achieved features are combined to obtain the fusion features containing the time-frequency domain information. The complementation of EEG and fNIRS features is achieved at multiple levels. Then, the 1DCNN is used to extract deep-level information from the fusion features. Finally, a fully connected neural network is used for classification. The proposed method has been tested with a public dataset. The EEG-fNIRS collaborative classification method achieves the accuracy of 95. 31% , which is 7. 81% ~ 9. 60% higher than those of single-modal signal classification methods. Experimental results show that this method fully integrates the timefrequency domain information of two physiologically complementary signals, and improves the classification accuracy of left and right hand grip tasks.

Abstract:Being a cutting-edge technique and hot topic in human-computer interaction, the haptic rendering provides bidirectional information and energy communication between human operators and the virtual environment ( VE) , which effectively enhances the level of reality and immersion and operating efficiency of virtual reality applications. This article proposes a novel two-dimensional (2D) haptic interface through electromagnetic force control, which consists of the two-dimensional background electromagnetic field generation and control module, fingertip-mounted permanent magnet, position tracking module, and central control module. The optimized parameter configuration for the electromagnetic coils and fingertip magnet is obtained with the ANSYS-based finite element analysis, which further reveals the relationship between the electromagnetic force, the driving currents for the 2D background magnetic field, and the position of the fingertip magnet. With the offline data for this relationship, a real-time interpolation-based force generation method is proposed for this 2D haptic rendering. Based on the proposed haptic interface prototype system, several experiments are carried out, including the force perception threshold experiment, and virtual object recognition experiment. Experimental results verify the efficacy of the proposed 2D haptic interface with the success rates for the two virtual object recognition experiments being 85. 7% and 71. 4% , respectively.

Abstract:At present, the clinical diagnosis of depression is mainly based on doctors′ experience and patients′ subjective feeling, which is highly subjective, low accuracy and time-consuming. With the development of neuron electrophysiology and computer technology, the objective classification and recognition of depression become possible. However, the existing research methods for the classification and identification of depression based on resting-state EEG signals are relatively simple, and it is necessary to further explore accurate, comprehensive and effective EEG features. In this article, a single-channel resting-state EEG depression classification and recognition method based on Higuchi′s Fractality Dimension (HFD) and Lempel-Ziv Complexity (LZC) is proposed based on the design of two experimental modes to obtain higher classification accuracy with fewer features. First, the resting-state EEG signals of 8 major depression disorders and 8 healthy control subjects are collected. Then, their nonlinear dynamic feature parameters HFD and LZC are extracted. Finally, the feature data are input into a nonlinear support vector machine model for classification recognition. Results show that the sensitivity, specificity and classification accuracy obtained by the combined feature are the highest at 98. 12% , 96. 67% and 95. 10% , respectively, which are 23. 05% , 17. 02% and 19. 29% higher than independent HFD/ LZC. Meanwhile, the main part of the model only takes about 12 s. The findings have important implications for the identification and auxiliary diagnosis of depression in clinical practice.

Abstract:There is the problem of motor imagery recognition in rehabilitation training. To address this issue, a feature extraction method of bilevel brain functional network based on the corticomuscular coupling node selection is proposed. According to the strength of corticomuscular coupling, the core nodes under each movement of the subjects are selected. Based on the core nodes and the prior knowledge of motor sensory brain region in neurophysiology, the motor sensory core node regional network is constructed and the features are extracted. By utilizing the whole network characteristic of minimum spanning tree, the diameter and average eccentricity of the minimum spanning tree are combined with the average node degree, average clustering coefficient and average path length of the core brain network. In this way, the comprehensive characteristics of global and regional functional brain network are constructed by the bilevel brain functional network. The support vector machine is selected as the classification method, and the average accuracy of two types motion imagery is 86. 96% , which confirms that the proposed bilevel brain functional network analysis method has excellent feature expression ability and can effectively extract the inherent neural-muscle correlation features. It provides a new idea for motor imagination recognition.

Abstract:A registration method based on ultrasound images clustering with the Gamma mixture model (UICG) is proposed to suppress the interference in the wall pulsation displacement estimation. A Gamma mixture model is used for the carotid artery B-mode ultrasound image clustering. Then, the normalized mutual information of the distant tissues is used as the similarity measure to extract interference curves. Based on the interference curves, the spatial inverse transformation of the clustering image sequence is implemented to eliminate the external interference. Finally, with the speckle tracking method, the pulsation displacement of the arterial wall is estimated from the sequence of the registered cluster images. Compared with the traditional registration algorithm, namely, the position weighted principal axis and centroid method combined with mutual information ( PCMI), simulations show that the normalized root mean square errors (NRMSEs) of the UICG-based interference estimations in X, Y, and rotation directions are reduced by 36% , 38% , and 32% , respectively. The mean NRMSE of the estimated wall displacements is decreased by 37% . The clinical trials based on the common carotid arteries of healthy subjects further evaluate the effectiveness of the UICG method. In summary, the UICG method can effectively suppress interference and thus improve the measurement accuracy of the wall displacement.

Abstract:The detection accuracy of time-frequency parameters in non-stationary harmonic analysis is low. To address this issue, a new method based on the adaptive variational mode decomposition (AVMD) and the improved energy operator is proposed. Firstly, AVMD is used to decompose the unsteady harmonic signal, in which the waveform feature matching method is used to extend the unsteady harmonic signal to reduce the influence of boundary effects. The energy difference and correlation coefficient are utilized as the modal decomposition factors in AVMD. Combined with the modal components, an improved sampling energy operator is proposed to quickly extract the instantaneous amplitude and frequency of harmonics, and complete the positioning of its start and end times according to the difference sum signal. In this way, the detection of time-frequency parameters of unsteady harmonics is realized. The simulation and actual measurement results show that the method can effectively complete the accurate detection of non-steady harmonics under the condition of power frequency fluctuation, inter-harmonic and noise interference, and realize the accurate positioning of transient harmonics. The maximum detection errors of non-steady harmonic frequency and amplitude are 0. 094 9% and 0. 931 4% .

Abstract:Real-time monitoring of blade vibration parameters is the key to ensure the healthy operation of aero-engines. The traditional blade tip timing sensor signal preprocessing method could produce a large timing error due to the wide range of rotor speed (20~ 20 000 r/ min), which affects the identification accuracy of blade vibration parameters. By analyzing the sample rate conversion model, a blade tip timing signal preprocessing method based on spatial transformation is proposed, which converts the equal-time sampling of the blade tip signal into equal-angle spatial sampling to achieve high-precision blade tip timing signal acquisition. The third-order Lagrange interpolation Farrow structure and through pipeline optimization techniques realize the balanced design of logic resource occupation and data processing rates on the field programmable gate array. Experiments show that this method can effectively complete the equal spatial angle sampling and reduce the influence of rotation speed change. When the real-time speed is 1 000 ~ 8 000 rpm and the reference speed is 3 000 rpm, the vibration displacement measurement error of this method is within 20. 19 μm, which is much lower than that of traditional displacement measurement methods. The measurement accuracy of arrival time and blade vibration displacement is improved.

Abstract:Fuel economy has always been a key performance indicator of automobiles, which is also an urgent problem to be addressed in environmental protection. At present, there are many methods for fuel consumption measurement. However, most of them only achieve monitoring of automobile fuel consumption, which cannot quickly and accurately evaluate the fuel economy of different models. In this article, based on real-time automotive controller area network bus data, a driving scenario method is proposed to divide the common driving process into short time units, classify and analyze automotive data under different driving states, establish evaluation models under each scenario, and design scoring methods according to the characteristics of each model to comprehensively evaluate the fuel economy of automobiles. The study shows that the evaluation results are not affected by driving environment or driving behavior. The fuel consumption calculation error is only 0. 4 after the prescribed driving time and road test, and the maximum evaluation error of the same car is only 4% . This method can provide car buyers with more accurate fuel economy evaluation reports for their models, avoid the inconvenience of field test drives, effectively improve the purchase rate of desirable vehicles, and can promote the emerging online car market.

Abstract:The multi-path effect caused by occlusion and small number of visible stars in the vehicle global satellite navigation system during driving result in poor positioning accuracy, an interactive multi model vehicle integrated navigation algorithm based on EM is proposed. In this article, the mixed Gaussian distribution model is used to describe the error distribution of GNSS multipath effect, and the SINS / GNSS integrated navigation subsystem information fusion method based on EM is proposed to estimate the offset error of multipath effect. The SINS / OD integrated navigation model based on zero speed constraint is formulated. Meanwhile, the interactive multi model algorithm is utilized to realize the interactive fusion of navigation models in the case of GNSS signal loss, which improves the accuracy of the vehicle integrated navigation system. Vehicle experiment results show that the proposed algorithm can effectively improve the navigation accuracy under the conditions of GNSS multipath effect and information loss. When the offset of Gaussian mixture model of multipath effect is 10 m, the offset estimation error is less than 0. 5 m, and the maximum horizontal positioning error is 2 m, which is 84. 62% lower than that of traditional interactive multi model algorithm.

Abstract:In view of the problems of matching pursuit algorithms in signal sparse decomposition, we propose a method to detect harmonics and inter-harmonics based on atomic decomposition, which is optimized by the imperialist competitive algorithm ( ICA) and orthogonal matching pursuit algorithm (OMP). First, the Gabor dictionary is simplified to the sinusoidal dictionary according to the characteristics of harmonics and inter-harmonics. Then, the signals are decomposed by using the OMP algorithm. In which, the number of iterations is determined by the reasonable threshold of correlation and energy. Finally, the estimation of parameters is obtained based on the index parameters of the most matching atoms. The introduction of ICA in the iterative process of OMP can search for the best matching atoms in the continuous parameter space, which avoids the limitation of index parameter step size on detection accuracy. Simulation results show that the proposed algorithm can detect each harmonic and inter-harmonic component with high accuracy, even under noisy conditions. The error of frequency, amplitude, and phase are less than 0. 015 4% , 0. 722 4% , and 1. 512 6°, respectively. In addition, the proposed algorithm has the ability to detect inter-harmonics with closing frequencies and locate the time-varying harmonics and inter-harmonics. Compared with the orthogonal matching pursuit algorithm, the computational complexity is reduced by more than 99% .

Abstract:The current technology for defect detection of composite curved components has problems of unintuitive detection results and low detection efficiency. To address these issues, a three-dimensional defect imaging method based on the phased array ultrasound is proposed. Three-dimensional scanning technology is used to obtain the surface model of the curved surface, and the parallel section method is used to design the detection trajectory. Then, the sequence of two-dimensional ultrasound data is collected by the phased array ultrasonic wheel probe. The polynomial method is used to fit the detection trajectory curve function. The normal vector and space coordinates of the two-dimensional ultrasound data are calculated according to the scan step length and the sequence relationship. The ultrasound point cloud set is generated. Finally, the voxelization down-sampling method is used to realize the three-dimensional imaging of the internal defects of the composite material. Experimental results show that the average error of defect imaging results between ultrasonic detection and CT detection is 1. 14 mm, which proves that the proposed method can quickly reconstruct the location, shape and size information of the defects, and accurately characterize of the internal defects of the composite material curved sample.

Abstract:The performance evaluation of the airborne infrared imaging system is very important for the task performance of aircraft. modulation transfer function (MTF) is a typical index to measure the performance of the infrared imaging system. At present, MTF measurement depends on specific image features and manual operation. Hence, it is difficult to achieve real-time and batch evaluation. In this article, an automatic MTF measurement method based on non-specific images is proposed. The method can automatically obtain MTF values from actual scene images without any specific features, which is realized through image edge detection, target region extraction, and the improved slanted edge method. Then, an equivalent physical model of the infrared imaging system is established to achieve MTF curve fitting and filter out random errors. Finally, a certain type of UAV infrared imaging system is taken as the verification object, and the absolute error between the MTF measured in the laboratory and the MTF calculated based on non-specific actual scene images is less than 0. 06, which proves the feasibility and effectiveness of the proposed method. This automatic measurement method not only can evaluate the MTF accurately in real time, but also provide a new performance testing idea for infrared imaging system which is inconvenient to conduct ground experiments.

Abstract:The dynamic signature verification has problems of the unequal length of dynamic features, complex dynamic signature verification methods, and low recognition rate. To address these issues, a dynamic signature verification method based on correlation coefficient is proposed. First, the feature weight sum in the corresponding region is filtered and calculated by dividing the original feature region. And the correlation coefficients between signature features are calculated by the Pearson correlation analysis method. Secondly, the Pearson correlation coefficient distribution of genuine and simulated signatures is analyzed with the correlation coefficient as a new feature. Finally, the signature is evaluated by combining the Gaussian density function model and setting an individual discrimination threshold. Experimental results show that the Pearson correlation coefficient inside the genuine signature is generally higher than that between the genuine and simulated signatures. This method shows better signature verification performance on SVC and xLongSignDB data sets. The false rejection rate and false acceptance rate on xLongSignDB data sets are 2. 1% and 1. 7% , respectively.