Journal of University of Chinese Academy of Sciences

Solutions of a class of stochastic Poisson systems

WANG Yuchao, WANG Lijin

2022, 39 (6): 721-726. DOI: 10.7523/j.ucas.2020.0057

In this paper, a class of stochastic Poisson systems, arising from randomly perturbing a type of Lotka-Volterra systems by certain Stratonovich white noise, are considered. We give the sufficient conditions for the almost sure existence (global non-explosion) and uniqueness of the solution of the system, and further prove that the solution is positive and bounded almost surely under the proposed conditions. Numeraical experiments are performed to verify the results.

Binding of lilrB2 and double-stranded Aβ(16-21)

WU Wenze, LI Xiaoyi

2022, 39 (6): 727-731. DOI: 10.7523/j.ucas.2020.0031

Abstract ( 500 ) PDF (0KB) ( 0 )

LilrB2 is a kind of protein targeted by Aβ (1-42) oligomers. The binding of them may result in some symptoms related to Alzheimer’s disease. It’s important to explore the binding mechanisms for the design of inhibitors. Three different conformations were designed by molecular docking of lilrB2 and double-stranded Aβ (16-21), and were used as the initial conformations for molecular dynamics simulations. Three sets of data were analyzed, finding that the ASP23 residue on lilrB2 had a strong interaction with LYS residue on the chain of Aβ (16-21), which had an obvious effect on the binding of receptor and ligand. In different binding states, the interaction of CYS142 residue with VAL or PHE on the chain of Aβ (16-21) and the interaction of SER24 residue with ALA on the chain of Aβ (16-21) are favorable for the binding of receptor and ligand.

Effects of soil nitrogen addition on photosynthesis of hybrid Broussonetia papyrifera in subtropical red soil area

LIU Ming, WANG Jingsheng

2022, 39 (6): 732-741. DOI: 10.7523/j.ucas.2021.0018

Abstract ( 563 ) PDF (0KB) ( 0 )

Soil nitrogen content and light conditions are important factors affecting plant photosynthesis. In order to quantify the relationship of soil nitrogen addition with photosynthesis and light response of hybrid Broussonetia papyrifera in subtropical red soil area, Li-6400 photosynthesis tester and other equipment were used to determine the photosynthetic process of hybrid Broussonetia papyrifera under the condition of soil nitrogen addition gradient at Qianyanzhou Experimental Station of Jiangxi Province, Chinese Academy of Sciences, and the light response curve of net photosynthetic rate was fitted by using the modified right-angle hyperbolic model. The results showed that: The light compensation point and light saturation point of hybrid Broussonetia papyrifera were 24.2 and 1 147.6 μmol·m^-2·s^-1, respectively, with strong adaptability to light environment. Hybrid Broussonetia papyrifera could maintain high photosynthetic efficiency at the light intensity of 800-1 200 μmol·m^-2·s^-1. Besides, adding 22.5 kg/ha of urea containing 46% nitrogen could increase the relative content of chlorophyll in hybrid Broussonetia papyrifera leaves by 12.8%, net photosynthetic rate by 66.8%. Soil nitrogen addition could significantly change chlorophyll fluorescence parameters such as maximum photochemical efficiency, actual photochemical efficiency, and apparent photosynthetic electron transfer rate, but it had no significant effect on photochemical quenching parameters and non-photochemical quenching parameters.

Simulation and prediction of urban heat island in Haikou City based on CA-Markov model

WANG Zi, MENG Qingyan, ZHANG Linlin, HU Die, YANG Tianliang

2022, 39 (6): 742-753. DOI: 10.7523/j.ucas.2021.0006

Abstract ( 767 ) PDF (0KB) ( 0 )

With rapid development of urbanization, great changes of surface coverage have intensified urban heat island effect. Using Landsat data, we analyzed the spatial variation of urban heat island in Haikou City, and CA-Markov model was applied to simulate and predict the trending of spatial distribution characteristics of the urban heat island. Moreover, we constructed a regression model between the urban heat island and the normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI). The results show that: 1) By using the CA-Markov model to simulate and predict the distribution of thermal environment in Haikou in 2016, the average error of each urban heat island intensity level was small, and the Kappa coefficient was 80.49%. The simulation accuracy was high, and the thermal environment distribution of Haikou in 2024 was obtained. 2) From 2000 to 2016, the heat island effect in Haikou became increasingly obvious, mainly extending along the Qiongzhou Strait, the west bank of the Nandu River, and around the high-speed rail. The extent of the intense heat island has increased by 11.60 km², and the extent of the heat island has decreased by 2.26 km² and remained roughly unchanged. The extent of the green island has increased by 38.64 km², which was the largest change in the urban heat island intensity in 16 years. It is predicted that the distribution of urban heat island intensity in 2024 will move toward the southeast direction. 3) In the multiple linear regression analysis, every increase of 0.1 in the NDVI index will reduce the difference in surface temperature between urban and rural areas by 0.22-0.45 ℃. And every 0.1 increase in NDBI index will cause a temperature difference of 0.20-1.42 ℃ in urban and rural areas. The results can provide scientific basis and reference for alleviating urban heat island effect and planning the future development direction of the cities.

Research on sustainable development goals of UNESCO designated sites in China based on multi-source data

WANG Pu, YANG Ruixia, LIANG Yongqi, CHEN Guolong, WANG Xinyuan

2022, 39 (6): 754-763. DOI: 10.7523/j.ucas.2021.0021

Abstract ( 354 ) PDF (0KB) ( 0 )

"Total expenditure (public and private) per capita spent on the preservation, protection and conservation of world heritage" is considered as an indicator to evaluate the sustainable development goals(SDGs) of world heritage by SDG11.4.1. It is in the state of having methods but no data, which needs the support of data and calculation cases. The statistical data of 48 UNESCO sites of China were used to measure and analyze SDG11.4.1, and Landsat images were used to monitor and analyze the human intervention of 4 natural heritage sites in this study. The results are shown as follows. 1) Expenditure per unit area can be used to measure the sustainable development status of natural protected areas (includes world natural heritage, mixed heritage, geopark and biosphere reserve), which is not suitable for the measurement of cultural heritage. 2) The average investment of 4 million yuan per square kilometer per year in natural protected area can be used as the threshold for high-quality development goals, and the average expenditure per square kilometer of 800 000 yuan per year can be used as the threshold for good development status. 3) The degree of human intervention as an extended index can reflect the sustainable protection status and trend of heritages, and can be further applied in similar protected sites in China and other countries.

Coherence analysis of high resolution SAR sub-aperture image and its application in ground feature classification

XING Wenji, JIN Yan, QIU Xiaolan, DING Chibiao, ZHOU Xiao

2022, 39 (6): 764-775. DOI: 10.7523/j.ucas.2021.0052

Abstract ( 722 ) PDF (0KB) ( 0 )

With the continuous improvement of synthetic aperture radar (SAR) resolution, the transmitted signal bandwidth and synthetic aperture are continuously increasing, which provide more options for subsequent applications. How to develop the potential of high resolution SAR with large synthetic aperture and large signal bandwidth in the application of ground feature classification and interference is worth studying. Coherence, the feature most commonly used in SAR image analysis, is analyzed in this paper. Firstly, the coherence coefficients between the sub-apertures, sub-bands, and repeat-pass interferometric sub-apertures of typical targets, such as man-made targets and natural features, are analyzed theoretically. Then, the above coherence coefficients are calculated using the real data of high-resolution spaceborne SAR to verify the correctness of the analysis. And then, unsupervised feature classifications are performed according to different features of different ground objects in different coherence coefficients, and the features represented by different categories were given. The analysis results in this paper provide support for the optimization application of high-resolution SAR data, and deepen the understanding of the characteristics of different SAR targets.

Polarimetric SAR image classification based on AdaBoost improved random forest and SVM

ZHANG Zheng, LI Shiqiang

2022, 39 (6): 776-782. DOI: 10.7523/j.ucas.2021.0020

Abstract ( 529 ) PDF (0KB) ( 0 )

In order to improve the classification accuracy of polarimetric synthetic aperture radar (SAR) images, a two-level classification structure based on AdaBoost improved random forest (RF) and support vector machine (SVM) is proposed. Firstly, the AdaBoost improved RF (ADA_RF) is taken as the first-level classifier, which can assign weights according to the classification abilities of the decision trees. ADA_RF assigns high weights to strong decision trees. The first-level classifier can also assess the importance of input features and compute a ranking list. Feature selection can be conducted according to the list. The SVM classifier is trained with the selected features to predict the second-level classification result. Finally, the neighborhood voting method is used to fuse the results. The comparison experiments of AIRSAR polarization data shows that the classification structure can effectively improve the classification accuracy of polarimetric SAR images.

SAR few-sample target recognition method based on convolutional block attention module and capsule network

HUO Xinyi, LI Yanlei, CHEN Longyong, ZHANG Fubo, SUN Wei

2022, 39 (6): 783-792. DOI: 10.7523/j.ucas.2021.0022

Abstract ( 508 ) PDF (0KB) ( 0 )

Synthetic aperture radar (SAR) target recognition has important research value in both military and civil fields. However, due to the high cost of SAR data acquisition and the small number of samples, the traditional convolutional neural network has insufficient ability to extract target features and low accuracy. This paper proposes a classification model combining convolutional attention and capsule network, which uses multi-dimensional vector neurons in the capsule network to represent more features of the target. At the same time, considering the lack of target feature information under the condition of small sample information, in order to improve the learning efficiency of the neural network, attention mechanism is added to the capsule network to guide the classification network to repeat the classification by learning the importance of different features, focusing on the features that contribute more to the classification results, and weakening the features that contribute less to the classification results. The experimental results on MSTAR data set and real vehicle data set show that the accuracy of the proposed algorithm is higher than those of the traditional convolution neural network and capsule network algorithm.

Research and experiment on containerized remote sensing information service platform technology

YAN Lei, LIU Wei, LIU Shibin, DUAN Jianbo, XIA Wei

2022, 39 (6): 793-800. DOI: 10.7523/j.ucas.2021.0013

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

Under the background of the era of remote sensing big data, combining remote sensing information with actual production has been widely used in all walks of life. With the processing and sharing of remote sensing information being applied to more and more fields, a single remote sensing data service architecture is no longer sufficient to meet the requirements of high availability and easy expansion in actual production conditions. China remote sensing satellite ground station has a huge amount of remote sensing image data, how to use existing data to provide better information services has always been the direction of ground station exploration. In this paper, under the private cloud environment, the containerized remote sensing information technology processing platform is constructed through kubernetes container arrangement to provide remote sensing information service. Furthermore, technical research is carried out in four aspects: the containerized basic environment, remote sensing image computing and processing, remote sensing data access and user service mode. A remote sensing information service platform integrating “data query and acquisition-image calculation processing-remote sensing information service” has been constructed.

A resource scheduling method for satellite mission ground station based on particle swarm optimization algorithm

FAN Huijing, ZHANG Wenyi, TIAN Miaomiao, MA Guangbin, CHENG Bo

2022, 39 (6): 801-808. DOI: 10.7523/j.ucas.2021.0012

Abstract ( 714 ) PDF (0KB) ( 0 )

In view of the problem of ground station resource scheduling for satellite data transmission and telemetry, tracing and control (TT&C) tasks, this paper proposes a modified particle swarm optimization (PSO) algorithm combined with heuristic methods to carry out integrated scheduling of satellite data transmission and TT&C tasks. Firstly, the constraints of satellite missions and ground station resources are analyzed, the constraint satisfaction model based on heuristic rules is established, and the superior initial population is filtered out. And then a modified PSO algorithm combining heuristic rules is designed to solve the resource scheduling problem. The simulation and comparison experiments show that the PSO algorithm has better ability to find excellence and convergence speed than traditional scheduling algorithm (such as genetic algorithm), and that the modified PSO algorithm has better ability to find excellence, convergence speed and stability than the traditional PSO algorithm.

A novel fast edge detection method in wide-band spectrum sensing

JIN Shuaichen, MA Hui, TANG Hongying, QIN Ronghua

2022, 39 (6): 809-816. DOI: 10.7523/j.ucas.2021.0007

Abstract ( 371 ) PDF (0KB) ( 0 )

The explosive growth of traffic demand in the era of internet of everything has resulted in the spectrum scarcity, which enabled a rapid development of dynamic spectrum access technologies in cognitive radio (CR). Fast and robust spectrum sensing method is evoked as the base of dynamic access. We optimized a wide-band spectrum sensing method based on energy detection. The optimized method can implement spectrum edge detection and channel state determination fast without any prior information (bandwidth, power spectrum density, etc.). Results show that our method is suitable for the wide-band blind spectrum sensing, and has an acceptable performance in low SNR scenario.

A Ceph write performance optimization method based on double-control nodes

HUANG Zunxiang, ZHU Leiji, XIONG Yong

2022, 39 (6): 817-826. DOI: 10.7523/j.ucas.2021.0051

Abstract ( 311 ) PDF (0KB) ( 8 )

Because the distributed storage system Ceph uses a multi-copy strong consistency write mechanism, the cluster write performance is not ideal. To solve this problem, this paper proposes a Ceph write optimization method based on double-control nodes. With double-control double-RAID nodes, when one controller fails, another partner controller in the node creates a new OSD process and quickly takes over the RAID of the failed controller, thereby ensuring the safety and high reliability of data storage. At the same time, the write mechanism is optimized as follows: after the primary OSD is written to the journal, the write completion is returned to the client. After that, the primary OSD continues to collect the completion status of the write data disk and other slave copies, and then completes callback operations. Thereby reducing the impact of unnecessary write operations on the write performance of the cluster. Finally, the data availability and cluster write performance are tested experimentally. The write performance test compares the optimized method and Ceph’s native write mechanism in terms of sequential write and random write from three perspectives of write latency, throughput and IOPS. It further verifies the effect of the optimization method on improving write performance while maintaining high data availability.

Two stream LSTM based on self-supervised learning for online action detection

ZHU Jiatong, QING Laiyun, HUANG Qingming

2022, 39 (6): 827-835. DOI: 10.7523/j.ucas.2021.0049

Abstract ( 710 ) PDF (0KB) ( 0 )

Online action detection plays very important role in many applications such as security and human-computer interaction. This mission requires that the system can detect the action when it just started, instead of waiting for the entire action comes to an end. Since in online action detection problem models can only make judgments based on the observed part of the video, so compared to other tasks such as action recognition and action detection, the model needs to dig out more from history information to assist decision-making for current frame. Based on the long short-term memory (LSTM) model commonly used in online action detection problems, this paper constructs a two-stream LSTM model called 2S-LSTM, and introduces the self-supervised learning idea, which is widely used in the image field, into the online action detection problem. First, the two-stream network 2S-LSTM model uses LSTM to model the temporal information of RGB flow and optical flow respectively. Moreover, based on the idea of self-supervised learning we construct two new loss functions:temporal similarity loss and optical flow stability loss for training. Experiments show that, compared with the past online motion detection methods such as RED, TRN, and IDN, our model in has achieved better results on both the TVSeries and THUMOS’14 datasets.

Visual object tracking based on multiple experts and MDNet

ZHANG Zhiming, LI Guorong, HUANG Qingming

2022, 39 (6): 836-844. DOI: 10.7523/j.ucas.2021.0002

Abstract ( 384 ) PDF (0KB) ( 0 )

In recent years, with the continuous development of deep learning technology, deep learning based visual object tracking algorithms have achieved great success. However, in the video, the background, illumination, and the appearance of the target are constantly changing, accompanied by the occurrence of occlusion. This brings great difficulties for visual object tracking. Most of the traditional methods tried to online update the tracker to adapt to the changes in the video. However, the content of the video is complex and changeable, and it is difficult to update and maintain one tracker online to deal with the complex data in the subsequent video, which can easily lead to the accumulation of errors. To solve this problem, based on the existing tracker MDNet, we propose a multi-expert tracker based tracing method. First, the common features of all targets in the video are learned through MDNet, so that the learned features can describe the target better. Then in the tracking process, multiple expert trackers are dynamically constructed according to the tracking results to increase the robustness of the trackers. Finally, the best expert tracker is selected according to the evaluation function of each expert and is used for tracking in the current frame. Experiments show that the proposed method achieves effective tracking results on 25 videos with abrupt changes. Compared with MDNet, the proposed method greatly improves the performance.

Resource allocation for SWIPT-D2D communication underlaying cellular networks

YANG Te, HONG Peilin, LI Runzhou

2022, 39 (6): 845-852. DOI: 10.7523/j.ucas.2021.0003

Abstract ( 458 ) PDF (0KB) ( 0 )

As an important radio frequency energy harvesting technology, simultaneous wireless information and power transfer (SWIPT) enables the energy-limited equipments continuously harvest radio frequency energy without interruption of communication. For the device-to-device (D2D) communication system underlaying cellular network, we consider the resource allocation problem when D2D receivers use power-splitting technology to decode information and harvest radio frequency energy simultaneously. In order to ensure the high information decoding rate of D2D receivers under high energy harvesting requirement, we propose a one-to-many spectrum multiplexing scheme for D2D communication. In this scheme, D2D devices are allowed to multiplex multiple cellular uplink spectrums for communication, and all user equipments in the network are used as distributed radio frequency energy sources to charge the D2D receivers. In this scenario, the system resource allocation including spectrum matching, power control and power-splitting control is jointly optimized to maximize the sum communication rate of D2D under the condition of ensuring the energy harvesting requirement of D2D receivers and the rate requirement of all communication links. The above optimization problem is a non-convex Mixed-Integer Nonlinear Programming (MINLP) problem, which is difficult to obtain the optimal solution. Therefore, we propose a two-layer resource allocation algorithm based on greedy thought and convex approximation theory. Simulation results show that compared with other resource allocation strategies, the proposed algorithm can significantly improve the sum rate of D2D communication under the premise of meeting the energy and communication rate requirements of user equipments.

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