Journal of University of Chinese Academy of Sciences

Relationships between Λ_φBV and BV(q,δ)

GE Ajun, WANG Heping, YAN Dunyan

2023, 40 (6): 721-725. DOI: 10.7523/j.ucas.2022.035

In this note, we obtain the sufficient and necessary conditions for the inclusions Λ_φBV⊂BV(q,δ), and BV(q,δ)⊂Λ_φBV is false when φ satisfies the Δ₂ condition.

Experimental study on the influences of mean interface temperature and pressure on thermal contact resistance of the material DD5 and 1Cr11Ni2W2MoV

WANG Dichang, LIAN Zengyan, WANG Pei, LIU Jie

2023, 40 (6): 726-734. DOI: 10.7523/j.ucas.2022.044

Abstract ( 406 ) PDF (0KB) ( 0 )

Measuring the thermal contact resistance for the material of key component in aero-engine is very essential for accurate evaluation of temperature distribution about engine, which in turn could provide important reference value for the optimization design of its component, control for tip clearance and thermal protection for material. The material DD5 and 1Cr11Ni2W2MoV, which have good comprehensive properties and excellent thermal fatigue as well as process attributes, are widely used in aero-engine turbine blade and casing, respectively. Based on the actual working conditions of the interface of the engine connecting parts, the thermal contact resistance for the above two materials are measured experimentally according to the steady-state heat flow method, and the influences of interface pressure between 45 MPa and 200 MPa, and temperature ranging from 150℃ and 300℃ on it are investigated. The research indicates that thermal contact resistance displays a power-law relationship with the interface pressure and temperature, and gradually decrease with the increase of them; under the same contact pressure and temperature, thermal contact resistance of the material 1Cr11Ni2W2MoV is smaller than that of DD5 while the gap between both progressively narrows as the pressure grows. In addition, the empirical formulas of thermal contact resistance with the interface pressure and temperature for the two materials are obtained, which could predict the experimental results well, and the relative error with 92.9% of the experimental data is less than 12%.

Grassland degradation assessment based on spatial heterogeneity in Kulusitai

GAO Ziqian, BAO Anming, ZHENG Guoxiong, HUANG Xiaoran, YU Tao, LIU Tie

2023, 40 (6): 735-742. DOI: 10.7523/j.ucas.2022.033

Abstract ( 456 ) PDF (0KB) ( 17 )

Based on Landsat data from 1990 to 2020, this study used the normalized difference vegetation index (NDVI) and spatial heterogeneity as indicators to monitor the grassland degradation in Kulusitai. The results showed that:1) During the study period, the NDVI in the central part of the study area exhibited an increasing trend, while the marginal area showed a decreasing trend. The spatial heterogeneity of grassland increased in the central and southwestern areas and decreased in the northern and southern areas. 2) About 54% of the grassland in the Kulusitai was degraded at different levels, 35% of grassland was improved and 10% of grassland showed regrowing conditions. 3) Temperature and precipitation had little influence on interannual variation of spatial heterogeneity. However, they showed significant impacts on NDVI yearly dynamics, in which precipitation was the dominant climate factor.

Lithology recognition of cuttings based on transfer learning

DONG Wenhao, ZHANG Huai

2023, 40 (6): 743-750. DOI: 10.7523/j.ucas.2022.026

Abstract ( 656 ) PDF (0KB) ( 0 )

Cutting logging plays an important role in the fields of geological structure research and oil and gas exploration. With the improvement of logging technology, the number of cutting logging pictures has increased sharply, and traditional manual identification of cuttings is far from meeting actual work requirements. Convolutional neural networks based on transfer learning are known for their high efficiency in image classification and recognition. This paper focuses on 18 common kinds of cuttings as the research object. Based on the VGG-16 model trained on the ImageNet image data set, a migration learning model conforming to the characteristics of the cuttings image data set is established and applied to the actual lithology recognition. This paper selects 5 877 cutting logging pictures, and the training set, validation set, and test set were randomly divided in the ratio of 3:1:1. The lithology recognition accuracies of the training set, validation set, and test set reach 98.6%, 87.2%, and 87.2%, respectively. The test results on learning show that this method is very effective in lithology classification and recognition.

First-principle calculations of CO₂ adsorption on the kaolinite (001) surface

LIANG Jiaxin, LIU Shanqi, LI Yongbing

2023, 40 (6): 751-760. DOI: 10.7523/j.ucas.2022.005

Abstract ( 451 ) PDF (0KB) ( 0 )

Adsorption is an important means of storing CO₂, and it is also an effective way to reduce the greenhouse effect caused by CO₂. Kaolinite, the main clay mineral, is one of the potential media to adsorb CO₂due to its large specific surface area, wide distribution and no pollution after CO₂ adsorption. In this paper, the first-principle calculations based on density functional theory were used to study the CO₂ adsorption on the kaolinite(001) surface. The stable configuration, electron transfer, Milliken population and partial density of states after adsorption are discussed. These results show that Hollow4-X is the most stable configuration with adsorption energy -0.41 eV in the adsorption configurations at different sites of Top(1-3), Bridge(1-3) and Hollow(1-6). In the stable adsorption configuration Hollow4-X, the hydrogen atom of the kaolinite(001) surface bonds with the oxygen atom of the CO₂, which leads to the formation of H-O bond. Moreover, the 2p orbital of the oxygen atom of CO₂ has a great contribution to the formation of H-O bond, and the electrons of the H atom of the kaolinite(001) surface transfer to the O atom of CO₂. Our calculations illustrate the mechanism of CO₂ adsorption on the kaolinite(001) surface from the atomic scale, and provide a basis for further research and utilization of kaolinite to store CO₂.

Ultra-high precision photometry and data analysis

SHI Yaqing, WANG Wei, ZHAO Jingkun

2023, 40 (6): 761-770. DOI: 10.7523/j.ucas.2022.034

Abstract ( 269 ) PDF (0KB) ( 0 )

Detailed characterization of extrasolar planetary atmosphere is one of the hottest and most challenging research topics nowadays, which relies on high precision photometric or spectroscopic data. Using our secondary eclipse observation of the hot Jupiter WASP-103b by the WIRCam on the CFHT as example, we describe the demands on instruments, observation strategy and data analysis to achieve ultra-high precision. With our techniques, it is proved to be feasible to reach a precision down to ~10^-4 using ground-based telescopes.

Combined algorithm for phased array antenna beamforming

ZHAO Minghao, ZHOU Yiguo

2023, 40 (6): 771-777. DOI: 10.7523/j.ucas.2022.022

Abstract ( 382 ) PDF (0KB) ( 0 )

A new combined algorithm is proposed in this paper to solve the problem that the algorithm convergence speed is slow and easy to fall into local convergence in complex beamforming. This algorithm is based on the Fourier transform relationship between the antenna pattern and the current of the array element. For the target beam pattern, the inverse Fourier transform is used to obtain amplitude and phase values of each element in the array antenna. The acquired values as the initial values are input into the genetic algorithm (GA). A complex beam pattern with wide null is finally designed by controlling only the phase. In order to prevent GA from falling into local optimum, chaos map is introduced. The initial population and mutation operation of GA are combined with chaos. With the help of ergodicity and randomness of sinusoidal chaos, the diversity of population and the ability of the algorithm to jump out of the local optimum are enhanced. Synthesis results show that the proposed algorithm can generate complex beam patterns with wide null, and it is not easy to fall into local optimal values.

Satellite image-based method for extracting potential surface dangers along power transmission lines

ZOU Shaoyue, LIU Shuo, XIA Hao

2023, 40 (6): 778-787. DOI: 10.7523/j.ucas.2022.018

Abstract ( 515 ) PDF (0KB) ( 0 )

Power transmission lines in China are faced with safety hazards brought about by a variety of surrounding features, and line hazard investigation is of great significance to ensure reliable national power supply and power security. At present, various methods of line hidden danger investigation face different difficulties, and satellite remote sensing technology can provide a low-cost, non-contact and rapid solution. In this paper, we use satellite remote sensing technology to realize the identification and extraction of hidden dangers on the ground along transmission lines. A method is also proposed for the identification and extraction of hidden hazards along transmission lines, using multi-category residual refinement module for result correction, which reduces the cases of poor recognition of small-scale targets and category edge misclassification, and lays the methodological foundation for the practical application of satellite remote sensing technology to the investigation of hidden dangers along power transmission lines.

SAR and optical image matching based on phase consistency calculation and RS-GLOH descriptor

JI Hongwei, LIU Chang, PAN Zhigang, SHEN Fangyu

2023, 40 (6): 788-799. DOI: 10.7523/j.ucas.2022.007

Abstract ( 459 ) PDF (0KB) ( 0 )

In order to solve the difficulty in matching between synthetic aperture radar (SAR) and optical images due to nonlinear radiation differences and geometric differences, a new feature-based matching algorithm for SAR and optical images is proposed in this paper. This algorithm is mainly divided into three stages:feature extraction, feature description and feature matching. In the feature extraction stage, according to the nonlinear radiation difference between the two kinds of image, the minimum and maximum moment maps of SAR and optical images are obtained using the method based on phase consistency, and the minimum moment point and maximum moment point are obtained through extreme point detection and non-maximum suppression. In the feature description stage, in view of the nonlinear geometric difference between the two images, the ROEWA operator with good speckle noise suppression effects and Sobel operator are used to calculate the gradient amplitude and direction information of the SAR and optical images, respectively. Then gradient location orientation histogram (GLOH) is set up to describe the feature points (RS-GLOH). In the feature matching stage, method of combining the nearest neighbor matching (NNDR) and fast sampling consistency (FSC) is used to match the minimum and maximum moments respectively point. Experimental results show that the proposed method has excellent rotation and scale invariance. Compared with PSO-SIFT, SAR-SIFT, and OS-SIFT, more correct matching points and lower root mean square error (RMSE) can be obtained in 5 sets of SAR and optical image pairs in different regions.

Road marking detection and extraction method based on neighborhood density and Kalman filter

LI Xiaoyu, ZHOU Mei, WANG Jinhu, YAO Qiangqiang

2023, 40 (6): 800-809. DOI: 10.7523/j.ucas.2022.024

Abstract ( 372 ) PDF (0KB) ( 0 )

This paper presents a methodology for detection and extraction of road marking based on neighborhood point density and Kalman filter from mobile laser scanning in three steps:1) Segmenting road point clouds into scan lines; 2) Generating convolution kernel based on point density in neighborhood for extracting road marking contour points; 3) Fitting contour line using least squares algorithm and completing omitted road markings using Kalman filter. A quantitative validation shows that the average deviation of center points and orientation are 0.04 m and 0.04°, respectively, and the average completeness of results are 99.69%. The proposed method reduces the influence of unevenly distributed points on road marking extraction and improves the overall extraction completeness.

An interpolation method for temperature telemetry data of one-dimensional low-sampling satellite panel based on SE-TCN

XU Kaikai, ZHANG Rui

2023, 40 (6): 810-820. DOI: 10.7523/j.ucas.2022.032

Abstract ( 409 ) PDF (0KB) ( 0 )

This paper proposes an autoregressive prediction method based on time convolutional network with attentional mechanism(time convolution network with squeeze and excitation, SE-TCN), to solve the problem of missing telemetry data of satellite panel temperature due to short entry time, framing error, and other reasons. Temperature telemetry data is considered to be a strong regularity of periodic signal, so this paper adopts the SE-TCN model to map from historical data to the data in the future, which completes the missing value interpolation and effectively solves the problem that the interpolation deviation of the physical model and statistical method is too large. At the same time, in order to characterize the interpolation effect on the actual missing data set, the calculation method of the evaluation index is added in this paper. Compared with long short-term memory network and time convolutiion network models, SE-TCN has a better interpolation effect on both the test set and the actual missing data set.

Fingerprint image quality evaluation algorithm based on visual perception model

FENG Qiliang, HAN Congying, ZHAO Tong

2023, 40 (6): 821-833. DOI: 10.7523/j.ucas.2022.020

Abstract ( 556 ) PDF (0KB) ( 13 )

Fingerprint is known as the first material evidence in the field of criminal investigation and forensic science, and plays a very important role in combating crime and maintaining social stability. The conclusion of fingerprint identification is the key to the formation of effective evidence and the detection of criminal cases, and the quality of fingerprint image directly affects the accuracy and reliability of fingerprint identification conclusions, so accurate and objective fingerprint image quality evaluation algorithm is an indispensable tool to assist fingerprint experts in fingerprint identification. At present, the NFIQ2.0 fingerprint image quality evaluation algorithm developed by the National Institute of Standards and Technology (NIST) has attracted extensive attention from experts and scholars at home and abroad. However, the quality evaluation results of this algorithm deviate greatly from the quality evaluation results of fingerprint experts, and lack of quality evaluation for local areas of fingerprint images. Therefore, it can not meet the needs of criminal investigation and forensic science fingerprint identification tasks. In this paper, the problem of fingerprint image quality evaluation is extended to the quality space, and the quality evaluation algorithm based on expert perception is proposed by learning the quality perception strategy of fingerprint experts for the local area of ridges. The experimental results show that the overall quality evaluation results of the proposed algorithm are consistent with the quality evaluation results of fingerprint experts, and accord with the application scenarios of criminal investigation and forensic science. In addition, this paper further compared with the NFIQ2.0 algorithm on several international public fingerprint data sets, and the results show that the quality evaluation score of the proposed algorithm is more reasonable, and can effectively reduce false non-match rate (FNMR) of the fingerprint matching algorithm.

Asset selection based on high frequency Sharpe ratio and robust correlation coefficient

ZHANG Shanhua, ZHANG Sanguo

2023, 40 (6): 834-842. DOI: 10.7523/j.ucas.2022.039

Abstract ( 502 ) PDF (0KB) ( 0 )

High frequency Sharpe ratio, a measure of return and risk, is commonly used in current portfolio construction method since it can avoid covariance matrix in high dimensional analysis. The newly proposed D-SEV measures the correlation between stock's return and high frequency Sharpe ratio index to further construct portfolio. However, there are some problems with the measure used in D-SEV, such as its lack of robustness and slow computational speed. In this paper, we propose to use a new correlation coefficient proposed by Sourav Chatterjee instead. The new correlation coefficient guarantee robustness, specifically it can reduce the impact of abnormal data on correlation, such as significant events that have a large impact on the asset prices. It is also extremely fast in its calculations. Extensive simulation demonstrate that new correlation coefficient outperforms D-SEV and other traditional methods in several different models. Actual Shanghai Securities Exchange (SSE) and Shenzhen Securities Exchange (SZSE) stock market data for 2019 and 2020 also show that the assets selected by new correlation coefficient earns 8% more excess annualized return than D-SEV, while it also owns a higher Sharpe ratio.

An automatic scheduling method and implementation of cryptographic evaluation tools

ZHANG Meng, WANG Pingjian, CHEN Tianyu

2023, 40 (6): 843-852. DOI: 10.7523/j.ucas.2022.043

Abstract ( 453 ) PDF (0KB) ( 0 )

In the process of cryptographic application evaluation, the evaluators complete the on-site evaluation and result analysis with the help of cryptographic evaluation tools. In practical application, the evaluators often need to use multiple evaluation tools in series. The output of one cryptographic evaluation tool needs to be used as the input of another tool to obtain further detection results. For example, when analyzing the SSL protocol, the digital certificate used for authentication should be extracted to complete the certificate format compliance verification. However, the existing evaluation tools are usually designed and developed independently for specific evaluation purposes, and they do not have the ability to work together with each other. The input and output data required by each tool still need evaluators to carry out manual collection, data conversion, import and export, which is time-consuming and labor-consuming, and it is easy to introduce manual errors in the process of processing data. This paper proposes a scheme of automatic scheduling platform for cryptographic evaluation tools. The scheme can automatically assemble according to the dependency between evaluation tools, schedule evaluation tasks in an orderly manner, collect evaluation intermediate data and schedule real-time data flow, output reports according to templates, and support three evaluation scenarios:product access, new system and system operation. Evaluators only need to upload the application scenario topology map of the evaluation object, identify checkpoints in the map, select the evaluation tool to be used, and then send scheduling instructions to the evaluation tool through the scheduling platform to complete the evaluation task. The scheduling platform adopts the network interface scheduling evaluation tool, which has scalability. The existing evaluation tools only need to be adapted and adjusted according to the unified interface model of evaluation tools proposed in this paper, and can be integrated into the scheduling platform to accept scheduling.

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