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Issue 5 ， 2020
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Yb-doped fiber MOPA system with radially polarized output beam based on LCP vortex retarder
Abstract：This study demonstrates the use of a Liquid Crystal Polymer (LCP) vortex retarder-based Yb-Doped Fiber (YDF) MOPA system with nanosecond pulse output to achieve efficient generation of a radially polarized beam from fiber lasers. The LCP vortex half-wave plate was employed as a mode converter after the MOPA laser to efficiently convert the high-peak-power, narrow-linewidth, linearly polarized nanosecond Gaussian pulse signal into a Laguerre-Gaussian beam with a hollow-ring distribution of transverse intensity using the spatial phase conversion method. When the MOPA system consisted of a narrow-linewidth continuous seed source, an electro-optical intensity modulator and a five-stage YDF amplifier were built up. A stable LP01 mode output with an average power of 20.1 W was obtained from the MOPA system. Subsequently, an LCP vortex half-wave plate was adopted as a spatial mode converter and a radially polarized beam output with a maximum average output power of 19.5 W, pulse width of 10 ns, and perfect hollow-ring transverse intensity was obtained at a repetition rate of 10 kHz. The mode purity measured by the PBS method is about 88.5%, which indicates that the radially polarized beam obtained has the advantages of high efficiency and high mode purity. This work can establish the foundation for the practical and highly efficient applications of radially polarized beams in the field of optical trapping, high-resolution imaging, and materials processing.Keywords：fiber laser;radially polarized beam;Master Oscillator Power-Amplifier (MOPA);space conversion of phase;high-power;high mode purity- 136
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Published：2020-07-09 -
Optical non-uniformity test of transparent infrared chalcogenide film and influencing factors
Abstract：The non-destructive testing of the non-uniformity of films is important in the preparation of large-area, high-quality infrared transparent films. In this study, a method is proposed to obtain the thickness and refractive index non-uniformity of the film. In the experiments, an infrared Ge-Sb-Se chalcogenide film was prepared on a silicon dioxide substrate using magnetron sputtering. The transmission spectra of 36 areas with a size of 80 μm×80 μm were measured using a microscopic Fourier infrared spectrometer. Further, the background noise was filtered out with the segmentation filtering method. Finally, the thickness and refractive index of each area were calculated using the improved Swanepoel method, and the non-uniformity of the film was accurately obtained by comparing the test results from different areas. The results indicate that the calculation accuracy of the film non-uniformity is better than 0.5%.Keywords：infrared film;improved Swanepoel method;optical non-uniformity;non-destructive testing- 53
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Published：2020-07-09 -
Optical system of bionic compound eye with large field of view
Abstract：Compound eyes are composed of several sub-eyes distributed on a curved surface. Such a vision system has several advantages, such as a small size, large Field of View (FOV), and high sensitivity, in addition to excellent application prospects in relevant fields of mechanical vision. To realize the additive manufacturing of a bionic compound eye system with a large FOV, the imaging principle of bionic compound eyes, design of a micro-lens array and relay system, and mechanical structure were studied. First, a single micro-lens was designed based on the optical principle of bionic compound eyes, and a micro-lens array was completed. Further, the curved image surface formed by the curved array was converted to a planar image, which could be received by a planar detector, by introducing a relay system. Subsequently, the micro-lens array and the relay system were combined, and the image quality was optimized. Finally, the mechanical structure was designed aiming at the additive manufacture of the compound eye system. The micro-lens array contains 3 481 closely-spliced hexagonal micro-lenses. Each lens has a FOV of 4° and a clear aperture of 110 μm, and the entire compound eye covered an FOV of 123.7°. The MTF values of each FOV are greater than 0.3 at 120 lp/mm, and the corresponding RMS spot radii are less than the radius of the Airy disk. The analysis results indicate that the compound eye with a require image quality can meet addictive manufacturing requirements.Keywords：optical system design;micro-lens array;bionic compound eye system;multi-aperture system- 159
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Published：2020-07-09 -
Optical properties of spring aerosol in typical areas of China
Abstract：The aim of this study is to determine aerosol optical characteristics in different regions, understand the differences in aerosol composition and source between regions, and establish a typical regional aerosol optical model. First, based on the direct and scattered solar radiation measured by POM02 solar radiometer and using skyrad algorithm, the aerosol optical parameters in spring in Delingha, Qinghai, Hefei, Anhui, and Maoming, Guangdong were obtained. These parameters include spectral distribution, refractive index, single scattering albedo, etc. Based on these parameters, the source and distribution characteristics of aerosols were analyzed. Likewise, the influence of dust weather on aerosol optical parameters was analyzed. The results show that the atmospheric cleanliness in Delingha area is high, 24% of optical thickness is distributed below 0.2, and the source of aerosol is single and dominated by large particles. Similarly, 39% of the wavelength index in Hefei area is distributed between 0.8—1.0, the real part of refractive index does not change significantly with wavelength, and the source of aerosol is complex and dominated by small particles. Finally, 54% of optical thickness in Maoming area is concentrated around 0.5, and the distribution area is complex stability. In dust weather, the increase of large particles is the main reason for the increase in aerosol optical thickness.Keywords：atmospheric optics;remote sensing;aerosol;radiometer- 103
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Published：2020-07-09 -
Turbulence spectrum separation of wind lidar using independent component analysis
Abstract：Research on spectrum separation in a turbulent environment using an Independent Component Analysis (ICA) algorithm of natural gradient descent is carried out to investigate the turbulence generated by heat or blades in giant wind turbines. When the main wind is distinguished from a mixed signal with turbulence, the efficiency of wind turbines in a wind field increases. Firstly, the backscattering characteristics and spectrum distribution of turbulence signals are analyzed. Then, the ICA model is designed according to these characteristics. As the simulation results meeting the requirements, a wind speed detection of outdoor binocular lidar antenna and the wind spectrum separation effect of the detection are carried out. The results show that under the condition with refractive index structure constantCn2≤10－14 and generalized atmospheric constant α≥4, the turbulence center and main wind speed can be separated using binocular signals. Statistical analysis of the fluctuation range of the two peaks in 1 s shows the estimated central wind speed of (2.59±0.05) MHz and estimated turbulence center of (1.22±0.19) MHz. The average signal-to-noise ratio of the two peaks is 25.93 dB and 31.01 dB, respectively, which meet the requirements for obtaining stable center wind speed and relatively stable turbulence center estimates.Keywords：lidar;wind measurement;turbulence;spectrum separation;Independent Component Analysis(ICA)- 93
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Published：2020-07-09 -
Grating eccentricity monitoring system for image-based angular displacement measurement
Abstract：In an image-based angular displacement measuring device, the installation eccentricity of the calibration grating directly affects the accuracy of angular displacement measurements. In this paper, a system for debugging the eccentricity of the grating in the angular displacement measuring device was designed. First, according to the mechanism of image-based angular displacement measurement, a method for monitoring the eccentricity of the calibration grating was proposed utilizing a linear array image sensor. A model of the eccentricity monitoring signal on the image sensor was subsequently established, and a mechanism for changing the signal was determined. Finally, an experiment on a certain type of angular displacement measurement device was carried out and debugging suggestions were provided. The experiment demonstrates that the adjustment reduces the root mean square error from 1 017″ to 12.8″.The eccentricity monitoring system designed in this paper can achieve installation and debugging of the calibration grating with high precision, and improve the efficiency of mass production of image-based angular displacement measurement devices.Keywords：angular displacement measurement;grating debugging;eccentricity error;monitoring system- 122
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Published：2020-07-09 -
Detection and classification of welding defects by magneto-optical imaging under alternating/rotating magnetic field
Abstract：Aiming at the difficult to detect arbitrary-angle weld defects, a Magneto-Optical (MO) imaging Non-Destructive Testing (NDT) system for weld defects excited by different magnetic fields was studied. The mechanism of the alternating magnetic field generated by the U-shaped yoke and the rotating magnetic field produced by the plane cross yoke was introduced. The MO imaging effects of different weld defects excited by alternating/rotating magnetic field were compared. The relationship between imaging characteristics of MO images and magnetic field strength was analyzed based on the Faraday rotation effect. The gray value of MO image can match the corresponding leakage magnetic field strength. The principal component analysis method was used to extract the grayscale features of the fused image column pixels and the texture features of the MO image were extracted by the gray-level co-occurrence matrix. A BP neural network model and a support vector machine model were established to identify these defect features. Experimental results show that the classification accuracy of the BP neural network model and the support vector machine model can reach 94.1% and 98.6% respectively under the excitation of rotating magnetic field. Compared with the alternating magnetic field, the classification accuracy is improved by 10.7% and 8.5%, respectively. MO imaging under rotating magnetic field excitation overcomes the limitation of directional detection of MO imaging under traditional magnetic field excitation, and realizes the detection and classification of arbitrary-angle weld defects.Keywords：magneto-optical imaging;weld defects;alternating/rotating magnetic field;texture feature- 71
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Published：2020-07-09
Modern Applied Optics
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Current harmonic suppression of permanent magnet synchronous linear motorbased on reduced-order double closed-loop ADRC
Abstract：The current harmonic components of permanent magnet synchronous linear motors are high due to back electromotive force and frequent switching of inverters. Moreover, disturbances such as time-varying parameters and sudden load changes seriously affect the control accuracy of servo systems. Therefore, a double closed-loop active disturbance rejection servo control algorithm, based on a reduced-order state observer, was presented to improve the harmonic suppression and control precision of control systems. First, the second-order active disturbance rejection controller of a position-speed loop was constructed. The pole placement method was used to reduce the order of the third-order linear extended state observer. This operation reduced the influence of phase lag and improves the control precision of the servo system. Second, the current loop first-order nonlinear active disturbance rejection controller was employed to eliminate the influence of integral saturation and reduce the harmonic content of the three-phase current. Finally, compared with other optimization algorithms based on active disturbance rejection control (ADRC), the experiments show that the THD of reduced-order double closed-loop ADRC is less than 2.13%, the thrust fluctuation can be reduced to 1.49%, and the steady-state error is less than 15 μm under multiple conditions.Keywords：permanent magnet synchronous linear motor;reduced-order state observer;double closed-loop control;current harmonic suppression- 310
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Published：2020-07-09 -
Nonlinear characteristics of silicon resonant accelerometer with vibrating beam integrated with comb fingers structure
Abstract：The nonlinear vibration of the silicon micro-resonator accelerometer can cause the vibration amplitude noise to couple to the frequency output and deteriorate the noise performance of the device. Therefore, it is necessary to evaluate the nonlinear vibration characteristics of the resonant accelerometer and optimize the design to extend the linear vibration range. In this paper, the simulation and experimental analysis of a Silicon micro-Resonant Accelerometer (SRA) based on comb-tooth structure and vibration beam were designed. Firstly, the nonlinear simulation analysis was carried out on the accelerometer structure using COMSOL simulation software. By applying a static force in the vibration direction of the resonant beam, the relationship between force and displacement is obtained, and the nonlinear cubic term coefficient k3, eff was calculated. The ratio of k3, eff to the linear coefficient keff is approximately 2.13 × 1010 m-2. Then the Double-End fixed-duty Tuning Fork (DETF) was subjected to frequency sweep test to obtain the nonlinear vibration frequency response curve of DETF. According to the Duffing equation, the experimental data is fitted. The ratio of the nonlinear cubic term coefficientk3, eff and the linear coefficient keff of the two DETFs of the device is 2.24×1010 m-2 and 2.19×1010 m-2. The errors between the simulated and tested values for k3, eff and keff are 5.2% and 2.8%, respectively. The experimental results agree well with the simulated values, which confirm the validity of the simulation method and the reliability of the test data. The designed resonant accelerometer was analyzed nonlinearly when the amplitude was less than 35.4 nm, DETF works in the linear region, which can provide reference for the design of the control circuit of the subsequent resonant add-on.Keywords：silicon micro-resonant accelerometer;nonlinear vibration;composite structure;simulation analysis- 91
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Published：2020-07-09 -
Shafting design for precise two-dimensional turntable applied to
Φ 300 mm plane mirrorAbstract：The optical inspection system for the on-orbit assembly telescope mainly includes the sub-aperture co-phasing inspection and wave aberration detection of the system, they share a Φ300 mm plane mirror, therefore, the precise switching of plane mirror should be realized. A set of two-dimensional turntable based on general P2 level bearing was designed to meet the precise switching requirement. Firstly, the structures of the shafting system were designed and the assembly process was described in detail; then, the theoretical calculation model was built to analyze the accuracy of the designed shafting system, the results show that the maximum shaking error of the horizontal shafting system is 2.36″(PV), and the maximum shaking error of the vertical shafting system is 0.56″(PV). Finally, the accuracy of the shafting system was tested by using the Fourier harmonic analysis method. the practice shows that the maximum shaking error of horizontal shafting system is 2.5″(PV), and the maximum shaking error of vertical shafting system is 0.6″(PV), the perpendicularity of the two shafts tested by the method of pair-addition reading is 1.5″. The test results verify the rationality of structural design and theoretical calculation model.Keywords：space telescope;on-orbit assembly;two-dimensional precise turntable;shafting error;Fourier harmonic analysis- 173
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Published：2020-07-09 -
Maximum torque per ampere current predictive control of interior permanent magnet synchronous motors based on parameter identification
Abstract：To realize an efficient and accurate control of Interior Permanent Magnet Synchronous Motors (IPMSMs) and solve the effects of the motor parameter changes on the control performance, a method for the Maximum Torque Per Ampere (MTPA) current predictive control of the IPMSM based on online parameter identification was proposed. First, according to the torque characteristic of the IPMSM, an optimal relationship ofd- and q-axis currents under the MTPA control was simplified to facilitate engineering calculation. The effects of the motor parameters on the MTPA operating point offset were analyzed. In addition, key parameters q-axis inductance and permanent magnet linkage of rotator, which significantly affect the MTPA algorithm, were adaptively identified based on a reference model to calculate the optimal d-q current distribution in real time. Subsequently, based on the accurately identified parameters and optimal current commands, predictive current control was applied such that the actual current can track the command faster and improve the dynamic performance of the system. The experimental results show that the errors of the online identification of q-axis inductance and permanent magnet linkage of rotator are less than 3% and 3.5%, respectively, and the convergence time is less than 20 ms. The motor can effectively track the MTPA operating point, and the current response time is less than 30 ms, which satisfies the requirements of stable, reliable, efficient, and fast operation of IPMSM systems.Keywords：Interior Permanent Magnet Synchronous Motor (IPMSM);Maximum Torque Per Ampere (MTPA);online identification;current prediction control;fast and efficient- 108
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Published：2020-07-09 -
Error analysis and compensation in position measurement of brake master cylinder compensation hole
Abstract：To address the problems regarding low efficiency, low accuracy, and high cost of brake master cylinder compensation hole measurement, a high-performance precision detection scheme based on optomechatronics was developed. After analyzing the error sources, an equation for error calculation with regard to brake master cylinder compensation hole measurement was derived. The analysis of the position measurement error led to the deduction of an incremental error compensation model, and an experiment was conducted for validation of the model. The experimental results show that the influence of vertical axis error on the measurement data for compensation hole diameter is minimal; however, it has significant influence on the position measurement data. The results show that the position accuracy of the compensation hole in ZDZG-20.64 standard part is improved by 0.05 mm and 0.254 mm. For ZDZG-22.2 standard part, the position accuracy is improved by 0.044 mm and 0.072 mm. The error model and compensation method can effectively improve the position detection accuracy of the brake master cylinder compensation hole.Keywords：optoelectronic measurement;brake master cylinder;vertical axis error;error analysis;incremental error compensation- 86
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Published：2020-07-09 -
Mechanism and test of air hammer instability of aerostatic bearing based on phase-induced vibration
Abstract：When the structure of an aerostatic bearing is inappropriate or the working parameters are not optimal, air hammer instability can occur easily. Hence, the mechanism of air hammer instability of aerostatic bearings based on phase-induced vibration was investigated in this study. Based on the phase relationship between the working pressure and air film thickness in aerostatic bearings with time, a theoretical model of the self-excited instability of the aerostatic bearings based on phase-induced vibration was established. Furthermore, the phase change of the working pressure and air film thickness of the aerostatic bearing were analyzed. The theoretical analysis and experimental results indicate that duringthe air hammer of the aerostatic thrust bearings, the working pressure phase and air film thickness constantly varies, and the air hammer occurs when the phase difference is 180°.This indicates that the cause of air hammer can be explained from the phase. This study further improves the mechanism of self-excited instability of the aerostatic bearings and provides a new method for the analysis of the mechanism and suppression of the air hammer instability of the aerostatic bearings.Keywords：aerostatic bearings;air hammer instability;phase-induced vibration;stability- 83
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Published：2020-07-09 -
Progress on performance promotion technologies of rotary traveling wave ultrasonic motors
Abstract：To spur the development and application of rotary traveling wave ultrasonic motors for precision driving, the progress regarding the performance promotion technologies and remaining unsolved technical problems were summarized. First, the operation mechanism was introduced to discuss the synthesis of traveling waves, the trajectory of particle elliptical motion, and friction drive at the contact interface. Then, structural improvements and the application of new materials were reviewed. The influence of increasing temperature was analyzed and the solution was discussed. By investigating the coupling relationship between the preload and the resonant frequency, the shortcomings of present studies were indicated. With the proposed driving mechanism of a dual traveling wave, rotary traveling wave ultrasonic motors could seize the opportunity to enhance the output performance, and the study progress and future directions were discussed. Finally, the challenges and feasible alternatives for rotary traveling wave ultrasonic motors were discussed.Keywords：ultrasonic motors;performance promotion;temperature rise;frequency shifting;dual traveling wave- 98
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Published：2020-07-09 -
Estimation of rotor speed using displacement signals in magnetic suspended flywheel
Abstract：To acquire the high-precision rotational speed information necessary to suppress unbalanced vibration ina magnetic suspension flywheel under the circumstance of speed sensors failure, a BP neural network model was constructed by pre-extracting the rotor displacement signal and the rotational speed signal to estimate the rotational speed in real time using the displacement signal. The magnetic suspension flywheel system model was constructed using MATLAB/Simulink. A neural network module was trained using simulated displacement and rotational speed data to estimate the rotational speed in real time, and the estimation results under constant speed and variable speed were obtained and compared with the system speed acquired from the speed sensor. The simulation and experimental results show that the speed estimation method demonstrates good estimation accuracy at both constant speed and variable speed, with an estimation error of less than 20 r/min throughout the experiment.Keywords：magnetic suspended flywheel;rotor speed estimation;Back Propagation(BP) neural network;unbalanced vibration;displacement signal- 91
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Published：2020-07-09 -
Fractional order modeling and experimental verification of hysteresis nonlinearities in piezoelectric actuators
Abstract：Hysteresis nonlinearities in the entire working range of piezoelectric ceramic actuators often result in reduced system accuracy, oscillations, and system instability. For a periodic sinusoidal input signal, a hysteresis modeling method based on a fractional-order operator was proposed herein. First, based on the analysis of piezoelectric and fractional-order operator characteristics, a fractional-order operator involving a simple structure and few parameters was used to describe the hysteresis characteristics of piezoelectric ceramics. Subsequently, a piezoelectric actuated micro-displacement positioning experimental platform based on dSpace was built. Finally, a fractional-order-operator-based hysteresis modeling method was applied to the piezoelectric actuated micro-displacement positioning platform to identify the hysteretic nonlinear characteristics of piezoelectric ceramics. The experimental results show that the Fractional-Order hysteresis Model (FOM) is superior to the traditional Prandtl-Ishlinskii Model (PIM) and the Enhanced Prandtl-Ishlinskii model (EPIM). In the low-frequency range, the precision of the FOM is slightly higher than that of the PIM and EPIM models; however, in the high-frequency range, the precision of the FOM model is significantly higher than that of the PIM and EPIM models. When the input frequency is 100 Hz, the accuracy of the proposed FOM is 69.84% and 68.88% higher than that of the PIM and EPIM models on the root mean square error, respectively.Keywords：piezoelectric ceramic actuator;hysteresis;fractional order;parameter identification- 121
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Published：2020-07-09 -
Development and application on hole-sealing of pL ultra-micro dispenser
Abstract：Adhesive bonding technology faces challenges regarding achievement of pL-magnitude ultra-micro dispensing in narrow spaces. To address this problem, a pL-magnitude ultra-micro automatic dispenser was designed in this study. The transfer-type sealing method was adopted, and a dispensing needle was driven to traverse through a glass microtube with loading glue; adherence was achieved with a micro-droplet. Subsequently, the micro-droplet could be partly transported to the target surface. The dispenser, which was 65 mm in total length and had a minimum resolution of 0.24 μm/step and a maximum stroke of 7 mm, was linked to the upper computer via a USB interface, using which the dispensing process was controlled. An experimental platform was constructed, and a manual micro-positioning platform with a resolution of 10 μm was used to load a micro-ball with a micro-hole drilled on its surface. Four CCD microscopes were used to detect the position of the micro-hole and provide visual feedback to the operator. An electronic micro-positioning platform with a resolution of 0.2 μm was also employed to hold the dispenser and transport it seamlessly to the correctposition for holesealing. After the hole-sealing process was completed, two optical microscopes were used to measure the size of the glue spot and assess the quality of the holesealing. Many experiments were conducted to study the performance of the dispenser. Subsequently, the dispenser was also used to seal micro-holes of diameters varying from 5 to 20μm in a narrow seal cavity with a diameter of 170 mm and length of 350 mm. Experimental results indicate that the glue dispenser enables excellent repeatability rates and effective covering and insertion for holesealing. When the dispensing needle′s size and typeas well as the moving speed and sealing method are adjusted, it can be used to seal micro-holes with pL-magnitude-volume glue in a narrow space at different pressures. The smallest experimental glue volume was 4.4 pL, which satisfies the requirement and enables effective sealing in a narrow space.Keywords：pL-magnitude;automatic dispenser;hole-sealing;narrow space- 86
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Published：2020-07-09
Micro/Nano Technology and Fine Mechanics
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Infrared-visible video automatic registration with contour feature matching
Abstract：To register infrared-visible video sequences precisely inalmost-planar scenes, an automatic registration method based on matching the contour features was proposed in this paper. This method could solve the challenging problem regarding extracting and matching features in multimodal images by iteratively matching the contour features of targets. First, this method adopted the technology of moving target detection to identify the contours of targets and extracted the contour feature points with the corner detection algorithm of Curvature Scale Space(CSS). Then, the global shape context descriptors and the local histogram of edge orientation descriptors were established to describe the features; theseareuseful forreliable feature matching. The matched feature pairs from different times were reserved in a reservoir based on the Gaussian distance criterion. Finally, to overcome the influence of target depth variationin almost-planar scenes, the loss function of the registration matrix was calculated by incorporating the strategy of randomly sampling foreground samples, after which the global registration matrix was updated. The method was validated using the LITIV dataset, and the results demonstrate that the proposed method outperforms state-of-the-art methods. The average overlap error of our method on nine test sequences is only 0.194;this value for the compared methods demonstrate a decrease of 18.5%. This essentially satisfies the precise requirement of infrared-visible video registration in almost-planar scenes, and this method is fairly robust.Keywords：infrared-visible video sequence;image registration;contour feature;feature matching;global registration matrix- 115
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Published：2020-07-09 -
Survey of target detection based on deep convolutional neural networks
Abstract：Object detection, which is a fundamental visual recognition problem in computer vision, has been extensively studied in the past few decades and has become one of the popular research areas in the world. The aim of object detection is to accurately locate specific objects in a given image and assign a corresponding label to each object. In recent years, Deep Convolutional Neural Networks (DCNN) have been used in a series of developments in object detection and image classification owing to their powerful capabilities of feature learning and transfer learning.It has garnered considerable attention in the field of computer vision for object detection. Therefore, the method of applying CNN in target detection to obtain better performance is an important topic for research.First, we reviewed and introduced several types of classic object detection algorithms.Next, we considered the generation process of the deep learning algorithm as a starting point, analyzed the technical ideas and key problems of DCNN in the application of target detection, and provided a comprehensive overview of various target detection methods in a systematic manner. Finally, in view of the major challenges in target detection and deep learning algorithms, we provided future development scope and direction to promote the study of target detection using deep learning.Keywords：image processing;deep convolutional neural network;object detection;feature representation;deep learning- 657
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Research on oil spill monitoring of multi-source remote sensing image based on deep semantic segmentation
Abstract：In remote sensing images, oil spill areasareusually affected by spot noise and uneven intensity, which leads to poor segmentation. A deep semantic segmentation method was introduced to combine a deep convolution neural network with a full connection conditional random field to form an end-to-end connection.Based on Resnet, first, the multi-source remote sensing image was roughly segmented as input by the deep convolutional neural network.Then, using Gaussian pairwise and mean field approximation, the conditional random field was established as the output of the recurrent neural network. The oil spill area on the sea surface was monitored by amulti-source remote sensing image and estimated by optical images. Experimental results show that the proposed method improves class ification accuracy and captures finer details of oil spill are ascompared with other models using the dataset established by the multi-source remote sensing image. The mean intersection over the union is 82.1%, and the monitoring effect is significantly improved.Keywords：spilled oil on the sea;Convolution Neural Network(CNN);semantic segmentation;conditional random field;remote sensing image- 167
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Published：2020-07-09 -
Special video classification based on multitask learning and multimodal feature fusion
Abstract：Classification of special videos is significant for intelligent surveillance of internet content. Existing algorithms that fuse multimodal features forclassification of special videoscannot measure multimodal audio-visual semantic correspondence.An algorithm for recognizing special videos based on multimodal audio-visual feature fusion was proposed herein over the framework of multitask learning. First, audio semantic features and spatial-temporal visual semantic cues, including appearance and motion, were extracted. A latent subspace to fuse audio and visual features whilst preserving their semantic information was learned and developed through jointly learning audio-visual semantic correspondence and special video classification. Subsequently, a multitask learning loss function was presented viacombination of the correspondence loss, obtained based on the measured audio-visual semantic information, and the cross-entropy loss of special video classification. Finally, an end-to-end intelligent system for special video recognition was implemented. Experimental results demonstrate that the accuracy of the proposed algorithm is 97.97% with respect to the Violent Flow dataset, and the average accuracy is 39.76% with respect to the Media Eval VSD 2015 dataset, where by the algorithm outperforms the other existing methods. These results show that the proposed algorithm is effective for improving the intelligence of network content surveillance.Keywords：special video recognition;feature extraction;multimodal feature fusion;semantic correspondence measurement;multitask learning- 98
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Recognition and segmentation of three-dimensional point cloud based on deep cascade convolutional neural network
Abstract：Three-dimensional (3D) object recognition and model semantic segmentation are widely appliedin fields such as automatic driving, robot navigation, 3D printing, and intelligent transportation. With a focuson the inability of PointNet++ to integrate contextual geometric structure information, a method for recognition and segmentation of 3D point cloud modes based on a deep cascade Convolutional Neural Network (CNN) was proposed herein. The deep semantic geometric features of the point cloud could be captured via construction of a deep dynamic graph CNN. Subsequently, the deep dynamic graph CNN was applied recursively as a subnetwork of a deep cascade CNN for nested partition of the input point set for full exploration of the fine-grained geometric features of the 3D model. Finally, to address the point cloud sampling nonuniformity problem in point set feature learning, a density adaptive layer was constructed.A recurrent neural network was used to encode the multiscale neighborhood features of each sample point to capture the contextual fine-grained geometric features. The experimental results showed that the recognition accuracy of this algorithm on ModelNet40 and ModelNet10 were 91.9% and 94.3%, respectively.The mean intersection-over-union on the ShapeNet Part, S3DIS, and vKITTI datasets was 85.6%, 58.3%, and 38.6%, respectively. This algorithm can improve the accuracy of 3D point cloud recognition and model semantic segmentation, and it shows high robustness.Keywords：three-dimensional(3D) point cloud;object recognition;semantic segmentation;convolutional neural network;recurrent neural network- 158
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Published：2020-07-09 -
Source separation based on improved two-dimensional variational mode decomposition
Abstract：Using the continuation method, it is difficult to determine the upward continuation height of the upward continuation and shortcomings of the deep source signal loss caused by continuation when the magnetic data of magnetic sources with different depths are separated. A separation method for magnetic data based on improved two-dimensional variational mode decomposition was proposed to solve the disadvantages associated with the continuation method. First, the magnetic data was separated for the first time using the best continuation height estimation method to obtain separated local and regional magnetic anomaly data.Subsequently, a two-dimensional variational modal decomposition was used to perform a second separation of the separated shallow local magnetic anomalies. The process did not need to calculate the upward continuation height and could automatically separate magnetic data at different frequencies.Finally, this method was used to identify magnetic sources with different depths. By separatingthe magnetic anomalyB z component and converting it into magnetic gradient tensor data, the magnetic gradient tensor data for magnetic sources with different depths could be separated, and the identification of magnetic sources with different depths was obtained. The experimental results show that the correlation between the separation data of small-scale magnetic sources (26 cm height difference) and observation data for a single target in an actual measurement is greater than 0.966 4.Compared with the traditional continuation separation method, the proposed method has a higher separation accuracy and stronger anti-interference ability.Keywords：continuation method;separation;two-dimensional variational mode decomposition;local anomaly;regional anomaly- 91
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Published：2020-07-09 -
FPGA-based hardware acceleration for CNNs developed using high-Level synthesis
Abstract：To accelerate the forward-propagation process of deep-learning networks, a field-programmable gate array (FPGA) hardware-acceleration system for AlexNet was developed using Vivado High-Level Synthesis (HLS), which can greatly reduce the FPGA development cost. Using Vivado HLS, developers can design hardware architectures on an FPGA platform using C/C++ code instead of a hardware-description language. We implemented AlexNet on an FPGA platform using the HLS tool, and then used the PIPELINE and ARRAY_PARTITION directives to optimize the proposed system. An evaluation of the proposed system shows that its performance is three times better than a traditional computing-platform graphics processing unit (GPU). In the future, owing to the high-level encapsulation, the developed system can be easily transformed into other convolutional neural networks for practical operation, which shows its great portability and practical application value.Keywords：deep learning;Field Programmable Gate Array (FPGA);high level synthesis;hardware acceleration circuits- 174
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Published：2020-07-09
Information Sciences
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