Table of Content

31 March 2022, Volume 40 Issue 2

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Communication Engineering

Refractive Index Measurement of Photopolymer Based on Digital Holographic Microscopy

LI Weixia, HUANG Sujuan, YAN Cheng, XIA Shengli, YIN Weihao

2022, 40(2):  179-189.  doi:10.3969/j.issn.0255-8297.2022.02.001

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A method based on the digital holographic microscopy (DHM) for measuring the refractive index of photopolymer is proposed. The digital hologram is recorded by the Mach-Zehnder off-axis interference system. The high-precision phase distribution is extracted by the hybrid reconstruction algorithm. Combining the profile distribution measured by optical profilometer, the refractive index of photopolymer is obtained by image registration. The evolution of refractive index of photopolymer during the UV curing process is quantitatively analyzed. The dynamic phase distribution of photopolymer is measured by DHM, and then the dynamic refractive index distribution is obtained. The relationship between the average refractive index and exposure time is plotted. The proposed method can be used to reveal the optical performance of microscopic optical devices and design high-quality optical systems.

Fracturing Monitoring of Oil-Wells Using Microstructured Optical Fiber Based Distributed Sensing

LIU Shuai, WANG Zhi, WANG Aiqing, LI Shijian, TU Dongsheng, ZHENG Yu, NUERMAIMAITI·Wumaierjiang

2022, 40(2):  190-203.  doi:10.3969/j.issn.0255-8297.2022.02.002

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This paper presents several high-temperature resistance polyimide coated microstructured optical fibers, with enhanced scatterings benefited from the specially designed fiber structures. Beyond that, these fibers also possess high resistance to humidity and a long lifetime in hydrogen-rich environments. Using one of these fibers, we study the scattering enhancement in a distributed sensor system. When the fiber loss is 3 dB/km, the measurement accuracy can be effectively improved by 2~5 times. Further testing this system for fracturing monitoring in oil wells, the collected data from the sensing system prove that the underneath fracturing can be monitored and be further used for supervising fracturing and temporary plugging operations.

Influence of Irradiation on Magneto-Optical Properties of Bi/Er/La Co-doped Active Silica Fiber

YE Le, WEN Jianxiang, ZENG Jiawei, DONG Yanhua, WANG Tingyun

2022, 40(2):  204-211.  doi:10.3969/j.issn.0255-8297.2022.02.003

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To improve the weak magneto-optical properties of standard single-mode fibers, in this paper, we fabricated a Bi/Er/La co-doped active silica fiber by using modified chemical vapor deposition (MCVD) method in combination with atomic layer deposition (ALD) technology. Furthermore, we processed the fiber with gamma-ray irradiation. A Verdet constant measurement system for fibers was built to study the magneto-optical properties of Bi/Er/La co-doped active silica fiber before and after irradiation. Experimental results show that the Verdet constant of Bi/Er/La co-doped active silica fiber is 1.02 rad/(T · m)at 1 310 nm, which is 36.2% higher than that of standard single-mode fiber. It is also shown that the processing of gamma-ray irradiation can improve the magneto-optical property of the fiber, and the Verdet constant of Bi/Er/La co-doped active silica fiber increases with the increase of irradiation dose. Especially under the irradiation dose of 3.0 kGy, the Verdet constant increases by 54.90%, whereas that of standard single-mode fibers increases only by 26.3% and gets saturation at the irradiation dose of 1.0 kGy.

Signal and Information Processing

Circle Center Detection and Correction Method of Circular Markers in Helicopter Blade Image

ZHANG Yubin, CHEN Yaofeng, LE Juan, CHENG Qiyou

2022, 40(2):  212-223.  doi:10.3969/j.issn.0255-8297.2022.02.004

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The deformation measurement of rotating helicopter high-speed rotor blades is usually based on circular markers. However, circular markers are usually with problems, such as low exposure, small target region, and asymmetric projection, which easily lead to detection missing of markers and location error of circle centers. In order to avoid these problems, a detection method based on circular markers in helicopter rotor blade images is proposed in this paper. Firstly, pixel coordinates of local gray extreme value centers in the image are extracted, and interferences are removed according to array arrangement structure, then pixel coordinates of all circular marker extreme values are obtained. Secondly, the circular region of interest (ROI) is established with each extremum coordinate as the center and the minimum distance from the adjacent extremum as the diameter. Within the ROI, the circle center is obtained by parallel watershed transformation and least square circle fitting. Thirdly, based on perspective transformation, the projection mapping relationship between the image and another helicopter rotor blade image, which with the same phase and perpendicular to the camera optical axis is established. And the projection mapping matrix is optimized by levenberg-marquardt (LM). Finally, the images is converted to a positive image for circle center fitting, and exact circle center coordinates are obtained by inverse transformation of circle center coordinates. Experimental results show that the accuracy and the precision of the proposed method are 98.89% and 0.191 mm, and it has been applied in the high-precision visual measurement of the motion trajectory and deformation of the helicopter high-speed rotor baled.

An OTSU Image Segmentation Method Based on Attribute Weighted Naive Bayesian Algorithm

MA Feihu, ZENG Cong, JIN Yichen, SUN Cuiyu, CHEN Huapeng

2022, 40(2):  224-232.  doi:10.3969/j.issn.0255-8297.2022.02.005

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In order to enhance the accuracy of image segmentation and optimize the detail segmentation effect of image segmentation, an improved OTSU image segmentation method based on attribute weighted naive Bayesian algorithm is proposed. The foreground and background of an image are selected according to the grayscale characteristics of the image as using OTSU algorithm, and then classified by using the attribute Weighted Naive Bayesian algorithm. Thus, the probability of the foreground and background of the image is calculated. By training this model to obtain the optimal threshold for the image segmentation process, the optimized effect of image segmentation can be obtained. Experiment with image data collected by drone aerial photography is conducted, and results show that the image segmentation of OTSU based on the attribute weighted naive Bayesian algorithm optimizes the image segmentation effect and shows much finer details of the image after segmentation, promising a prospective application value.

Tensor Model Based on Total Variation Regularized and L_2,1 Norm for Video Rain Streaks Removal

LU Xinghan, ZHENG Yuhui, ZHANG Jianwei

2022, 40(2):  233-245.  doi:10.3969/j.issn.0255-8297.2022.02.006

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This paper proposes a tensor model for video rain streaks removal based on total variational regularization and L_2,1 norm to solve rain streaks shielding. Firstly, the prior information of rain streaks component and the video background are preprocessed to obtain the corresponding regularization condition, so as to enhance the sparsity of each part and facilitate the separation of rain streaks. Then, considering the existence of irregular dynamic objects in the video, a total variational regularization term is introduced to suppress the variation of background intensity and alleviate the misjudgment of rain streaks. The alternating direction method of multipliers (ADMM) can be used to effectively solve the proposed tensor model, and carried out a large number of experiments on the synthetic and real datasets. Experimental results show that the proposed method under dynamic background can effectively remove the video rain streaks and retain more background details simultaneously. Compared with other relevant methods, the proposed method has great advantages in three comprehensive quantitative performance measures of peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and residual error (RES).

Infrared Image ROI Encryption Method Based on Lorenz Chaos System

WANG Congli, PING Xijian, ZHANG Tao

2022, 40(2):  246-252.  doi:10.3969/j.issn.0255-8297.2022.02.007

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In order to improve the security of monitoring system and the speed of full image encrypting, based on the fast image-block encryption method, the concept of Region of interest encryption for infrared image is advanced in this paper. The OSTU algorithm are used to extract the ROI for encryption, then a Lorenz sequence is processed in order to ensure the randomness. Based on the random Lorenz data sequence, the ROI encryption is processed. Compared to the other encryption methods, the Lorenz ROI encryption has better performance on operation speed, security and encryption effect.

Reversible Data Hiding Based on Multi-Pair Asymmetric Histogram Modification

HE Yufen, TANG Jin, YIN Zhaoxia

2022, 40(2):  253-265.  doi:10.3969/j.issn.0255-8297.2022.02.008

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There are many invalid modifications of pixels in traditional reversible image information hiding algorithms based on asymmetric histogram modification. To solve this problem, a reversible information hiding scheme based on multi-pair asymmetric prediction error histogram modification is proposed in this paper. This scheme combines the advantages of asymmetric histogram modification algorithm and the characteristics of multihistogram modification algorithm, and selects the smooth region of the image to modify the asymmetric histogram for embedding information. At the same time of achieving the compensation reduction effect of asymmetric histogram, it can further reduce the invalid pixel modification and improve the rate distortion performance.

An Improved ORB Algorithm Based on Quad-Tree Partition

NI Cui, WANG Peng, SUN Hao, LI Qian

2022, 40(2):  266-278.  doi:10.3969/j.issn.0255-8297.2022.02.009

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The image feature points extracted by the original ORB algorithm often appear the phenomenon of "clustering and overlapping", and their distribution is relatively dense and lack of scale invariance, which easily leads to the problem of mismatching of image feature points. In order to solve this problem, this paper proposes an image feature point extraction algorithm based on quad-tree structure. First, the scale pyramid of the image is built, and then the quad-tree is used to divide the image, and the depth of the partition is limited. The FAST algorithm is employed to detect the feature points of the scaled image by multiple detection thresholds. Second, the ORB feature points will be extracted based on the division of the total sub-block and the total number of the feature points. And then the best feature points are obtained by taking the maximum inhibition method, and the feature points' descriptors are calculated with the help of the improved BRIEF algorithm. Finally the work of feature points matching will be realized. Experimental results show that compared with the original ORB algorithm, the uniformity of feature points extracted by the proposed algorithm in this paper is significantly improved. The number of redundant and overlapping feature points is reduced, and the extraction speed of feature points is improved by more than 30%.

Image High Resolution Reconstruction Algorithm Using Directional Cycle Spinning Operations in Wavelet Domain

ABUDURUSULI Aosiman, AILIMINUER Abulijiang, ZULIHAYETI Aihemaiti

2022, 40(2):  279-287.  doi:10.3969/j.issn.0255-8297.2022.02.010

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Traditional image interpolation algorithm is good at global handling of images, but weak in dealing with edge details of images, thus leading to sawtooth lines in detail areas. For this problem, this paper proposes an image resolution and contrast enhancement algorithm. First, a high-resolution image is obtained from an original one by using wavelet zero padding algorithm, and the lost edge and texture features of the image are compensated by using residual error correction process. Then, directional cycle spinning operations are performed on the image. Considering that the high frequency components in horizontal, vertical and diagonal directions of the image after wavelet decomposition can reflect the edge changes in the image, we use high frequency components in different directions of the image to describe mutation degrees in corresponding directions of image pixels. According to the mutation degrees, an adaptive fusion process of cycle spinning operations is realized, which can avoid excessive suppression of edge details. Finally, a nonlinear enhancement function is used to improve image contrast and highlight edge and contour information, Experimental results show that this algorithm not only enhances the spatial resolution and contrast of images, but also retains the edge and contour information contained in original ones. Compared with other image interpolation algorithms, this algorithm shows improved performs both in visual effect and anti-noise ability.

Computer Science and Applications

Photovoltaic Power Forecast Improved Stacking Algorithm

LI Pengqin, ZHANG Changsheng, LI Yingna, LI Chuan

2022, 40(2):  288-301.  doi:10.3969/j.issn.0255-8297.2022.02.011

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Stacking algorithm is good at alleviating over fitting problem in the prediction of photovoltaic power generation, but with drawbacks of long computation time and less sample data. To solve the problem, this paper proposes an improved 3-layer stacking algorithm based on new vector representation and cross validation accuracy weighting. The first and second layers are the primary layer, which use random forest, SVR and XGboost3. The third layer is the secondary layer, and uses LightGBM to learn the output of the second layer again to reduce noise. A new vector representation method is used to increase the sample size and sample distribution density of input and output data between levels to ensure that the data dimension will not increase with the increase of the number of primary level learners. At the same time, the results are weighted according to the difference in the prediction accuracy of different prediction models in the primary layer under cross-validation. Practical analysis is demonstrated by using the power generation data of a photovoltaic power station. Compared with random forest model and Stacking model, the prediction performance of the proposed model has been greatly improved in MAE, MSE and R-Squared.

Air Quality Prediction Based on MLP&ST Model

ZHENG Hong, CHENG Yunhui, HU Yangsheng, HUANG Jianhua

2022, 40(2):  302-315.  doi:10.3969/j.issn.0255-8297.2022.02.012

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Air quality prediction system is necessary in guarding human and environment health, and the historical air pollution data collected by monitoring stations in a city and advanced computer equipment makes it possible to forecast air quality in a data-driven way. However, most of the existing methods are suitable for forecasting the air quality in monitored areas, and only few are for unmonitored areas. In this paper, a joint training model MLP&ST (MLP&Spatial-Temporal) is proposed which takes the consideration of the comprehensive impact of meteorological factors, spatial correlation and time dependence on air quality, to predict the future air quality index (AQI) of unmonitored areas in Beijing. Compared with several other air quality forecast models, the optimized historical time step P value of 29 is experimentally obtained. Experimental results show that the hybrid model is superior to other models in performance indices (RMSE, MAE and MAPE) and has good ability of predictability.

Control of Uncertain Bilateral Teleoperation System under DOS Attack

ZHENG Kaizhong, FAN Chunxia

2022, 40(2):  316-327.  doi:10.3969/j.issn.0255-8297.2022.02.013

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The stability of bilateral teleoperating system with time-delay and uncertain model parameters under denial of service (DoS) attack is studied. First, based on position error structures, event-triggered controllers can be designed, and then the unknown model parameters of manipulators are estimated by using the adaptive law. The stability of bilateral teleoperating system is proved by Lyapunov function theory, and the event-triggered controller does not have Zeno behavior. Numerical simulations are carried out to demonstrate the effectiveness of the proposed adaptive event-triggered controller on a bilateral teleoperation system under DOS attacks.

3D Point Cloud Classification and Segmentation Network Based on Local Feature Enhancement

CHEN Lifang, WEI Mengru

2022, 40(2):  328-337.  doi:10.3969/j.issn.0255-8297.2022.02.014

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In the process of point cloud processing, many deep learning networks fail to fully consider the complicated relationships between local points, resulting in the loss of a large number of spatial geometric information. To solve this problem, an enriching local features network for point cloud classification and segmentation is proposed. The network designs an encoding unit to encode the multi-directional information of points, applies the attention mechanism to learn features of the local points formed after sampling and grouping, and proposes a new multi-dimensional loss function, which combines cross entropy loss function and the center loss function to act on the classification task. Extensive experiments are carried out on Modelnet40 and ScanNet datasets. The experimental results show that the network performs well in object classification and semantic segmentation tasks of 3D point cloud.

Impact of Epidemic on Consumer Economy via Transaction Data

PAN Jing, CHAI Hongfeng, QIN Zheng, SUN Quan, GAO Pengfei, ZHENG Jianbin

2022, 40(2):  338-348.  doi:10.3969/j.issn.0255-8297.2022.02.015

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The sudden COVID-19 pandemic has led to unprecedented impact on China's economic and social development, especially on consumer economy. Payment is the most closely related economic activity regarding consumers, the data of which can accurately capture the characteristic of the consumer economy. Given the point of view, this paper makes an in-depth exploration in the research direction of payment data. Based on the transaction data from China UnionPay, this paper quantifies the impact of the epidemic on consumer economy and makes a comparative analysis of different provinces and industries. The paper quantitatively reveals that the epidemic progress significantly affects the consumer confidence and the development of consumer economy. According to our analysis, it is unwise to restart work resumption when the epidemic is still not under effective control. From the perspective of payment, this paper profoundly tells about the story of how China fights against the epidemic and puts forward relevant suggestions for the follow-up epidemic prevention and control as well as the comprehensive economic recovery policies.

Energy-Saving Scheduling Algorithm for Multi-Variable Neighborhood Based on Pruning Optimization

QIU Bin, SUN Manman, CUI Suli

2022, 40(2):  349-360.  doi:10.3969/j.issn.0255-8297.2022.02.016

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In order to improve the energy-saving level of task scheduling in heterogeneous computer systems, a multi-variable neighborhood energy-saving scheduling algorithm fused with pruning optimization is proposed. Processor constraint and time constraint neighborhood structures are constructed in the algorithm. The number of redundant processors is reduced by constraining the neighborhood of processors, thus lowering the overall energy consumption. Pruning optimization is introduced into the time and energy consumption neighborhood to improve the efficiency of local optimization. Simulation results show that the proposed algorithm achieves good energy-saving effect under different problem scales, processor capacity and communication ratio.