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Research Articles
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Curing process of epoxy resin (E-51) and 3-(2-amino-2-oxoethyl)-1-butyl imidazolium dicyanamide
- GAO Sheng, LIU Long, ZHANG Yanqiang, GUI Dayong, JIANG Zhiyi
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2020, 37 (3):
289-294.
DOI: 10.7523/j.issn.2095-6134.2020.03.001
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Abstract (
222 )
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In this work, ionic liquids of 3-(2-amino-2-oxoethyl)-1-butyl imidazolium dicyanamide (AOBD) was used as curing agent for bisphenol A epoxy resin (E-51). The curing processes and the thermoset properties of AOBD/E-51 were fully characterized. Results show that AOBD is a high temperature curing agent for E-51, the optimum curing ratio and temperature range of AOBD/E-51 are 10:100 and 122-156℃, respectively, and the post-curing temperature is 178℃. The mechanical properties of the thermosets were characterized as follows. The tensile strength is 30.7 MPa, the tensile modulus is 3 233.1 MPa, and the elongation at break is 1.1%. The dynamic mechanical thermal analysis results show that the maximum loss factor, the glass transition temperature, the storage modulus, and the crosslinking density are 0.56, 174.8℃, 134.8 MPa, and 3 198.8 mol·m-3, respectively. Based on the IR spectra in different curing processes, the -CONH2 groups of AOBD react with epoxy groups at lower temperature, and then the anions react with epoxy groups further at higher temperature to achieve the whole curing processes. This study provides informative guidelines for choosing new curing agents of epoxy resin.
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Regional differences in the coupling relationship between Chinese economic growth and carbon emissions based on decoupling index and LMDI
- ZHOU Yannan, YANG Yu, CHENG Bo, HUANG Jixia
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2020, 37 (3):
295-307.
DOI: 10.7523/j.issn.2095-6134.2020.03.002
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Abstract (
883 )
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To investigate regional differences in the coupling relationship between Chinese economic growth and carbon emissions, we selected 29 provinces and regions in China as the research units and developed the decoupling index to study the dynamic coupling relationship. Further, we analyzed the changes in carbon emissions in different regions from the economic scale growth, structural transformation, and technological upgrading aspects using the method of LMDI (logarithmic mean Divisia index). The results of the study for the period from 1990 to 2014 are given as follows. 1) With the ever increasing of Chinese economy as well as carbon emissions, the carbon emission intensity reduced, showing a trend of "green transition" as a whole. 2) The 29 provinces in China were mainly in the absolute decoupling, relative decoupling, and expansive negative decoupling states, and the coupling relationship between economic development and carbon emissions evolved with time passing. 3) In the course of Chinese economic development, the growth of economic scale drove the growth of carbon emissions, and the effects of technology progress and industrial structure varied from region to region. In different periods, the dominant effects of carbon emissions in various regions of China were also different. Economic scale effect and technical effect were the dominant effect, while the structure effect had the least impact.
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Landslide change and its influence factors in central Nepal from 2001 to 2017
- SUN Guoqing, CHEN Fang, YU Bo, WANG Ning
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2020, 37 (3):
308-316.
DOI: 10.7523/j.issn.2095-6134.2020.03.003
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Abstract (
380 )
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Landslide is one of the main geological disasters which cause huge economic losses and casualties in Nepal. Remote sensing provides great potential to identify, extract, and monitor landslide information. Based on the average impurity reduction algorithm of random forest model, this study uses indices including NDBI, MNDWI, NDVI, and red band from Landsat images to extract landslide information. Further extraction of landslide information is obtained by visual interpretation. In this study, data grid method is used to divide the study area into 0.05°×0.05° grids. Based on this method, the distribution of landslides and correlation and partial correlation of landslides with rainfall and temperature are analyzed for different types of land cover. The results are shown as follows. of research show that:1) The landslides in central Nepal are mainly distributed at elevation between 1 000 and 2 500 m and at the slope gradient between 20° and 40°. 2) Compared with other types of land cover, the correlation coefficient between the number of landslides and the rainfall is 0.671 5 in forest land, while the number of landslides has the strongest partial correlation with temperature in grassland and the coefficient is 0.436 1. 3) The extensive distributions of gneiss and slate, together with earthquake, the fault zone, and the human activities, are destroying the stability of the slope and exposing the study area to be highly prone to landslide disasters.
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A relocation method for early archaeological excavated sites and a case study
- GENG Tong, YANG Ruixia, YANG Shugang
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2020, 37 (3):
317-323.
DOI: 10.7523/j.issn.2095-6134.2020.03.004
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Abstract (
331 )
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Spatial location is an important attribute of an archaeological site. The early archaeological excavation in China was limited by technical equipments and other factors. Some excavated and backfilled sites lack accurate spatial location information, which causes major inconvenience to follow-up research and protection. In this work, a relocation model for early archaeological excavated sites was established, based on GIS technology and multi-geographic information including existing archaeological excavation records, data from references, high-resolution remote sensing images, and early maps. The model is tested at Mengzhuang Site in Huixian County, Henan Province. The results show that the method significantly reduces the ambiguity and uncertainty of the site location and distribution, and hence this study provides support for archaeological site reconnaissance, protection planning, and restudy.
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Numerical simulation of regional magmatic hydrothermal mineralization: a case study on the Qianhe gold deposits in Xiong'ershan region
- MIN Lingshuai, CHENG Huihong, SHI Yaolin
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2020, 37 (3):
324-335.
DOI: 10.7523/j.issn.2095-6134.2020.03.005
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Abstract (
419 )
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The Qianhe gold mine is one of the most representative gold deposits in the Xiong'ershan region, which is located in the southern margin of North China Craton and is also an important gold producing region in China. The deposit of Qianhe is a medium-low temperature altered catallactic rock type Au deposit. The ore-forming fluid is mainly magmatic hydrothermal fluid, and the ore-forming material source is mainly from deep mantle source. In this research, we take the Qianhe gold deposit as an example to simulate the dynamic mineralization process. Through establishing a two-dimensional geological model, the steady-state and transient numerical simulations are carried out, respectively. Simulation results show that high-temperature ore-forming fluid from magma flows along the fault zone, and it heats the fault and surrounding rock achieving the temperature and pressure condition of mineralization. Moreover, total heat transferred by fluid is related to fault width, fluid temperature and pressure at the source of hydrothermal fluid, and the fault permeability. The higher the permeability of faults and the greater the pressure and temperature at the source are, the higher the hydrothermal velocity and advection heat flow density in faults are. Under the same heat flow density, the wider the faults and the greater the total heat transferred are, the more favorable the formation of gold deposits is in a relatively short period time to reach the temperature favorable to the formation of gold deposits under a specific pressure. We find that the fault permeability which varies several orders of magnitude has the most significant effect among these influencing factors. When the fault permeability is low, the hydrothermal metallogenic system in Qianhe area would need about 1 Ma to reach a stable and suitable metallogenic condition, while it needs only a millennium to reach a long and stable temperature and pressure metallogenic condition when the fault permeability is high.
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Study of syringe-free droplet microfluidic platforms enabling stable single-cell encapsulation
- FAN Beiyuan, LIU Lixing, LI Xiufeng, CHEN Deyong, WANG Wenhui, WANG Junbo, CHEN Jian
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2020, 37 (3):
336-344.
DOI: 10.7523/j.issn.2095-6134.2020.03.006
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Abstract (
362 )
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Negative pressures were used to generate droplets for single-cell encapsulations. Iodixanol was used to change the suspension solution densities in order to reduce the issue of cellular sedimentations. The effects of negative pressures and geometries of microfluidic T-channels on volumes and frequencies of generated droplets and the effects of iodixanol on the percentages of single-cell encapsulations were investigated and compared. This microfluidic platform may enable stable single-cell encapsulations, paving ways for single-cell analysis.
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A vegetation filtering method for rock mass point clouds based on multi-dimensionality features and MLP
- HU Liang, XIAO Jun, WANG Ying
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2020, 37 (3):
345-351.
DOI: 10.7523/j.issn.2095-6134.2020.03.007
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Abstract (
272 )
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Filtering on rock mass point clouds is an important step in 3D rock mass reconstruction. This work focuses on rock mass point clouds and we propose a vegetation filtering method based on multi-dimensionality features and MLP(multi-layer perceptron). This method firstly calculates multi-dimensionality features for each point. Then, MLP is used for training the classifier, which can be applied in vegetation filtering. We analyze the availability of multi-dimensionality features and select the best MLP model through different experimental processes. The experimental results show that the proposed method has a higher precision than other classifiers and it can be better applied in the field of rock mass point cloud vegetation filtering.
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Extraction of steel plants based on optimized SSD network incorporating negative sample's multi channels
- LU Kaixuan, LI Guoqing, CHEN Zhengchao, ZAN Luyang, LI Baipeng, GAO Jianwei
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2020, 37 (3):
352-359.
DOI: 10.7523/j.issn.2095-6134.2020.03.008
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Abstract (
351 )
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It is important to accurately detect steel plants for capacity reduction monitoring and environmental protection. The traditional method is time-consuming and laborious, and can not be used to monitor the steel plants in large areas. We propose a stable and accurate method by adding a maxout module to SSD, namely, transforming the negative sample path into a multi-branch structure. The neural network learns abundant features of hard negative samples, and thereby increases resistance to the useless features. Meanwhile, we used the well-trained model to detect steel plants in the Jing-Jin-Ji area based on GF-1 data. The results were compared with the data of the steel plants obtained from visual interpretation. Our method detects steel plants in the Jing-Jin-Ji area with an accuracy of more than 80%.
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Automatic extraction of tailing pond based on SSD of deep learning
- YAN Kai, SHEN Ting, CHEN Zhengchao, YAN Hongxuan
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2020, 37 (3):
360-367.
DOI: 10.7523/j.issn.2095-6134.2020.03.009
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Abstract (
402 )
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In order to automatically extract tailing ponds in North China, the SSD target detection model based on deep learning is applied to extract tailing ponds from remote sensing images. Firstly, 2 000 samples in North China were labeled as the foundation of database. 1 500 samples were randomly selected as training samples, and the remaining samples were used as test samples to verify the detection accuracy of the model. By using the original SSD model, the targets of large tailing ponds can not be detected accurately. In this work the relationship between the corresponding receptive field of convolution layer and the size of tailing reservoir in image was analyzed. Moreover, in order to improve the detection accuracy of the model for large-scale tailing pond targets, we modified the structure of SSD model by introducing an extra convolution layer. Experiments show that, compared with the original SSD model, the modified SSD model improves the detection accuracy by 10% and the recall rate by 14.4% at 0.3 confidence level. The detection accuracy for large tailing ponds is also improved. In this work, the feasibility of automatic extraction of tailing reservoir based on deep learning SSD model and the effectiveness of the modified algorithm were verified.
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A method of multi-ship target detection and tracking by on-orbit satellite
- JIAO Tengzhang, HU Yuxin, Lü Peng, ZHANG Kai, TAI Xianqing
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2020, 37 (3):
368-378.
DOI: 10.7523/j.issn.2095-6134.2020.03.010
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Abstract (
490 )
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The traditional method of remote sensing satellite image processing utilizes target detection and target tracking on the ground. The satellite transmits the image data to the data processing center on the ground, and the ground data processing system implements target detection and tracking based on the received remote sensing image data. However, with the spatial resolution increase of remote sensing images, the amount of data needed to be transmitted increases greatly. This results in a significant increase in ground-to-satellite data transmission time. Ultimately the efficiency of target detection and tracking reduces. In order to solve the problem, we propose a method based on the multi-feature Canny edge detection algorithm and the joint probability data association algorithm for moving multi-ship detection and tracking by on-orbit satellite. The proposed method is validated by using real data of an optical remote sensing satellite on satellite-based embedded development platform. The results show that the proposed method detects and tracks the multiple ships quickly and accurately.
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Cooperative spectrum sensing based on neural network in cognitive radio
- XUE Jianwei, TANG Liang, BU Zhiyong
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2020, 37 (3):
379-386.
DOI: 10.7523/j.issn.2095-6134.2020.03.011
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Abstract (
325 )
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Cognitive radio is a technology that can be used to effectively alleviate the current strain of spectrum resources, and spectrum sensing is the prerequisite of cognitive radio. To overcome the poor performance under the condition of low SNR, a cooperative spectrum sensing algorithm combining the high-order cumulants of signal and the eigenvalue of covariance matrix with the neural network is proposed. The algorithm takes into account the channel fading between the cognitive users and primary users and utilizes the strong multi-classification ability of neural networks. The ratio of maximum-minimum eigenvalues, the ratio of average-minimum eigenvalues, and high-order cumulants are used as the inputs of neural networks to realize the spectrum sensing. The simulation results show that the proposed algorithm not only has higher spectrum detection rate than other algorithms at low SNR, but also identifies the modulation type of the signal.
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Fast exact classification algorithm of massive fingerprint patterns based on capsule network
- LI Bonan, ZHAO Tong, WU Min
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2020, 37 (3):
387-397.
DOI: 10.7523/j.issn.2095-6134.2020.03.012
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Abstract (
394 )
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Fingerprint identification has been regarded as the most reliable biometric identification method, which has been widely used in the fields of criminal investigation, resident identification, and immigration personnel identification. The feature of these applications is that one query fingerprint needs to be quickly and accurately compared with all the fingerprints in the mass fingerprint database to identify the owner of the fingerprint. In recent years, the size of the fingerprint database was expanded rapidly with the law of fingerprint collection coming into effect. For one thing the image difference bewteen fingerprints in the same type increases significantly, for another the similarity of different types of fingerprint images also increases, which makes the misclassification rate of fingerprint classification algorithm greatly increase. At the same time, "massive fingerprint precision classification" has become a hot spot in the field of public security application and fingerprint identification. Aiming at solving the above problems, we propose an accurate fingerprint classification mode Caps-FingerNet based on capsule networks. On the one hand, the mode combines the unique network characteristics of the capsule networks with the individual self-similar texture features of fingerprint images to form a more robust feature extractor and classifier. Meanwhile, the batch-normalization method avoids the shortcoming of gradient disappearance in typical capsule networks. On the other hand, the attention capsule mechanism is innovatively introduced in this study, making Caps-FingerNet more accurate and comprehensive than typical capsule networks in extracting fingerprint image details, and the global squashing algorithm is used to squash the capsule effectively. Cap-FingerNet achieves an extreme accuracy of 99.63% in the four-class fingerprint classification of a provincial public security criminal fingerprint database. The accuracy rates of 96.25% and 94.5% were measured on the four and five classification tasks of NIST-DB04 fingerprint dataset, respectively.
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Analysis for influencing factors of real estate price in Hefei based on spatial network auto-regressive transformation model
- ZHOU Jiaqi, JIN Baisuo
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2020, 37 (3):
398-404.
DOI: 10.7523/j.issn.2095-6134.2020.03.013
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Abstract (
219 )
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The transaction data of ordinary residential house prices in Hefei City from 2016 to 2017 was considered. By using the spatial interpolation method and trend analysis method, the spatial changes in residential prices were analyzed. It was found that the house prices in Hefei gradually decreased from south to north and decreased from the center to the edge districts in the east-west direction. Expanding the two-phase change-point estimation method of Jin et al. and using the new change-point detection algorithm we found a change point which divided the residential price into two intervals, and we analyzed separately to establish a spatial lag model. The research results show that the residential prices in Baohe District show a strong spatial auto-correlation, and there are obvious spatial agglomeration characteristics. It is better to build a spatial lag model by finding out the change points and then separately building the spatial network auto-regressive models. There are many factors that affect house prices. Business districts, subway stations, school districts, plot ratios, and total floor area all have certain impacts on prices.
Brief Reports
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Photoregulating RNA transcription using azobenzene modified hairpin DNA
- CHEN Lu, JI Heming, MO Mengwu, LEI Huajun, BU Xinya, HE Yujian, FENG Lutian, WU Li
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2020, 37 (3):
416-423.
DOI: 10.7523/j.issn.2095-6134.2020.03.015
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Abstract (
304 )
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Effects of light isomerization of azobenzene on the structure and function of biological macromolecules is a hot topic of research in recent years. We focus on the transcriptional process of genetic information transmission and study the light regulation of azobenzene on RNA polymerase reaction. We modified 4,4'-dimethylol azobenzene at the T7 promoter end of the non-template strand of RNA transcription and ligated the 4 protection chains with length of 5, 7, 9, and 11 nt. Their photoisomerization ability and light regulation of RNA transcription were explored. It was found that the azobenzene-modified non-template strand DNA template reacted with T7 RNA polymerase to increase transcription efficiency under UV light response. RNA transcripts were designed. The 5 nt length T7NC increased from approximately 3.4% before illumination to 21.4% after illumination, with a 6.25-fold change. Similarly, for DNA templates containing T7NC1, T7NC2, and T7NC3, the RNA transcripts directed by UV light also increased, with the increase efficiencies of 4, 3, and 1.25 fold, respectively. Therefore, the non-templates we designed have the best light control effect on RNA transcription guided by a combination of a 5 base hairpin structure and a template strand. This discovery of the photoactive modification of azobenzene in the template sequence involved in life activities can be used as a future scientific tool and to guide the application of nucleic acid drugs.
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Study on age-friendly communities against the background of aging society: a case study of 4 communities in Nanjing City
- XU Liting, CHEN Weixiao, XU Chen, ZHANG Lulu, YAO Shimou
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2020, 37 (3):
424-431.
DOI: 10.7523/j.issn.2095-6134.2020.03.016
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Abstract (
339 )
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With the increasing aging degree in the national population of China, the age-friendly level of the community has attracted wide attention at home and abroad. This study summarized theoretical connotation and research progress of the age-friendly level of the community, built the evaluation index system based on Maslow's hierarchy theory of needs, and investigated the demand situation of the elderly under the guidance of the social evacuation theory. Four typical communities in Nanjing City were selected for the empirical research. The results showed that age-friendly level of the community was mainly affected by the geographical location, construction time, and community type. The closer to the city center the community was the higher the age-friendly level. The newly constructed community had a higher age-friendly level than the old one. The high-grade commercial community usually had high age-friendly level, while the resettlement community had low age-friendly level. With the increase of age, the activities of the elderly groups were more and more concentrated on the community space, and the elderly's dependence on the community service facilities was increasing. In order to adapt to the deepening aging trend, it is necessary to fully consider the wishes of the elderly group on the basis of the current situation of the age-friendly level of the community.
