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Journal of Jilin University (Information Science Edition)
ISSN 1671-5896
CN 22-1344/TN
主 任:田宏志
编 辑:张 洁 刘冬亮 刘俏亮
    赵浩宇
电 话:0431-5152552
E-mail:nhxb@jlu.edu.cn
地 址:长春市东南湖大路5377号
    (130012)
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Table of Content
20 January 2020, Volume 38 Issue 1
High Dynamic GNSS Anti-Jamming Algorithms Based on Nulling Widening and Deepening#br#
CONG Yuliang, FENG Da, LI Honglei
Journal of Jilin University (Information Science Edition). 2020, 38 (1):  1-8. 
Abstract ( 583 )   PDF (821KB) ( 216 )  
In order to solve the problem that the depth of the adaptive anti-jamming algorithm becomes
shallow after the null-pit broadening under high dynamic conditions,a null-pit broadening and deepening
algorithm is proposed,in which the disturbance obeys the Gauss statistical distribution. The algorithm
preprocesses the data by projection transformation,extracts the interference components from the sampled
signal,enhances the interference components by weighting the coefficients,constructs a new covariance
matrix on the basis of the null-pitch broadening algorithm,and finally uses the power inversion algorithm to
suppress the interference to achieve the purpose of null-pitch broadening and deepening. The simulation
results show that under high dynamic conditions,the depth of the proposed algorithm is increased on the
basis of the null broadening algorithm,and the robustness of the null broadening anti-jamming algorithm is
improved.
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VMD De-Noising Method Based on Cloud Similarity Measurement
ZHOU Yina, LU Jingyi, DONG Hongli, ZHANG Yong
Journal of Jilin University (Information Science Edition). 2020, 38 (1):  9-17. 
Abstract ( 373 )   PDF (1416KB) ( 301 )  
In order to distinguish the high frequency and low frequency modal components after VMD
( Variational Mode Decomposition) decomposition and improve the de-noising performance of VMD algorithm,a
de-noising method based on cloud similarity measurement is proposed. Firstly,the signal is decomposed by
VMD. The effective component and the noisy component are distinguished by calculating the cloud similarity
between each modal component and the signal. Then the noisy component is filtered by wavelet transform.
Finally,the denoised mode components and the effective component are reconstructed. Through simulation and
practical experiments,the proposed denoising method,VMD denoising method based on correlation coefficient
and VMD denoising method based on mutual information are used to process the noise signal. The SNR ( Signalto-
Noise Ratio) obtained by the proposed method is relatively high,which is 28. 214 1 dB. The mean square
error is relatively low,which is 6. 12 × 104,which verifies the superiority of the proposed method and the
feasibility of denoising the leakage signal of oil and gas pipelines.
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2D-ATFPF Seismic Denoising Algorithm Based on Adam Optimization
MENG Fanlei, FAN Qinyin, MU Lihong
Journal of Jilin University (Information Science Edition). 2020, 38 (1):  18-26. 
Abstract ( 312 )   PDF (2195KB) ( 212 )  
It is difficult to get a compromise between amplitude protection and denoising by using fixed window
length in TFPF ( Time-Frequency Peak Filtering) Algorithm,and the signal beyond the cut-off frequency can
not be tracked. Moreover,the traditional TFPF only filters along the time direction,ignoring the spatial
information of the signal. To solve these problems,a 2D-ATFPF ( Two-Dimensional Adaptive TFPF) algorithm
is proposed. Firstly,a set of TFPF impulse responses determined by different window functions are used to
construct convex sets of filtering results. Then,an objective function for filtering results is introduced under the
convex set,which is based on the least square criterion and takes the directional derivative with spatiotemporal
correlation as a penalty term. Finally,the optimization of objective function uses a fast convergent projection
Adam method. The application of 2D-ATFPF in synthetic recording and real data show that the new method can
restore the event and the the signal-to-noise ratio has increased about 1. 3 dB compared to the one-dimensional
traditional algorithm.
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Research of Hybrid WMN Inter-Domain Mobility Management Scheme
LI Zhijun, LI Peiri, LIU Dan
Journal of Jilin University (Information Science Edition). 2020, 38 (1):  27-33. 
Abstract ( 251 )   PDF (1681KB) ( 112 )  
To solve the problem of network congestion caused by broadcasting PREQ ( Path Request) messages
and proxy update messages across the hybrid wireless mesh network in the inter-domain mobility management
scheme of HWMP ( Hybrid Wireless Mesh Protocol) protocol,a new routing update algorithm for new nodes in
proactive routing mode of HWMP protocol is proposed,which restricts sending PREQ messages to some extent,
improves the mechanism of inter-domain mobility management of HWMP protocol and reduces the number of
routing update packets in the whole network. It reduces the end-to-end delay of the network. The simulation
results show that the AHWMP ( Advanced Hybrid Wireless Mesh Protocol) scheme has better performance than
that of HWMP protocol in terms of average network throughput and average end-to-end delay.
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Modulation Signal Feature Extraction Algorithm in Radio Monitoring
LI Lin
Journal of Jilin University (Information Science Edition). 2020, 38 (1):  34-41. 
Abstract ( 461 )   PDF (313KB) ( 257 )  
Aiming at the problem that the traditional modulation signal feature extraction algorithm has low
recognition accuracy and poor classification effect in noisy environment,based on the existing modulation signal
processing method,a new feature extraction algorithm for modulation signals in radio monitoring is proposed. The
mathematical models of all kinds of modulation signals in radio monitoring are constructed firstly,based on which
the characteristics of instantaneous amplitude,instantaneous phase and instantaneous frequency are obtained by
simulation. The advantages and disadvantages of various algorithms are analyzed for the recognition of the current
signal modulation mode,using the wavelet transform to complete the noise reduction processing of the modulation
signal and the design of the algorithm for the feature extraction of the abrupt boundary,use the algorithm for the
maximum feature extraction of the spectral density of the zero center normalized instantaneous amplitude and the
kernel discriminant analysis algorithm to extract all kinds of modulation signals layer by layer. The complete
classification and improvement of all kinds of modulation signals are realized. The extraction accuracy of the
feature signal under the influence of noise and the classification effect are improved,which provides a favorable
scientific basis for the feature extraction of modulation signals in radio monitoring.

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Improved Vector Control of SVPWM Asynchronous Motor
FU Guangjie, ZHANG Xudong, SHI Yingchen, ZHANG Mengdi, JIANG Yuze
Journal of Jilin University (Information Science Edition). 2020, 38 (1):  42-48. 
Abstract ( 344 )   PDF (1268KB) ( 310 )  
An improved SVPWM( Space Vector Pulse Width Modulation) control algorithm is proposed for the oil
field three-phase asynchronous motor with low control precision,slow response,and large calculation of basic
SVPWM calculation time. The fan is calculated by rotating the reference vector of some sectors. The action time
of the zone 1 and the sector 6 save the calculation of the action time of other sectors and simplify the calculation
amount. By constructing the vector control model of the asynchronous motor,the simulation analysis of the load
characteristics on the pumping unit verifies that the speed tracking is strong,the overshoot is small,and the flux
linkage is small,especially for the application scenarios with sudden load changes it has certain superiority.
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Image Denoising Based on Memristive Pulse Coupled Neural Network
GAO Hongyu, HUANG Wenli, DONG Hongli, LI Jiahui
Journal of Jilin University (Information Science Edition). 2020, 38 (1):  49-54. 
Abstract ( 342 )   PDF (1461KB) ( 259 )  
In order to solve the parameter immobilization of the traditional pulse-coupled neural network,the
memory properties of the memristive components are applied in the image processing,and two memristive
components in the anti-parallel are used in the connection strength analog between the neurons of pulse neural
network. A novel memristive pulse neural network is constructed to realize the dynamic change of the connection
strength between neurons,and the new network is used for image denoising. The Matlab simulation experiment is
carried out to verify the good performance of the improved new network in image denoising. The peak signal-tonoise
ratio and image similarity index prove that the method has good effect on image denoising.
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Corroded Rivet Classification Based on Tree-CNN
TANG Lu, WANG Congqing
Journal of Jilin University (Information Science Edition). 2020, 38 (1):  55-63. 
Abstract ( 392 )   PDF (1723KB) ( 456 )  
Considering that the accuracy of classification in corroded rivets is low and manual inspection is the
main method,a Tree-CNN ( Convolutional Neural Networks) classification method is proposed. This method is
specially designed for classifying corroded rivets on aircrafts. In order to improve the classification accuracy of
Tree-CNN method,the structure of the tree is determined by the confusion matrix of rivet categories which is
calculated in normal CNN method. The depth of the tree is three for five-classification of corroded rivets.
Experimental results show that by using the Tree-CNN method,the accuracy of classifying corroded rivets can
reach up to 86. 5%,which is effective in classification in corroded rivets.
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Classification of EEG Signals Using Local Mean Decomposition#br# and Iterative Random Forest#br#
QIN Xiwen, GUO Yu, DONG Xiaogang, GUO Jiajing, YUAN Di
Journal of Jilin University (Information Science Edition). 2020, 38 (1):  64-71. 
Abstract ( 280 )   PDF (857KB) ( 136 )  
In order to achieve effective identification of EEG( Electroencephalogram) signals in patients with
epilepsy,improve the quality of life for patients,a method of EEG signal classification based on the combination
of local mean decomposition and iterative random forest is proposed for the non-stationary and nonlinear
characteristics of EEG signals. Firstly,the EEG signal is decomposed into several product function components
and a residual component by using local mean decomposition. Then all components are extracted and classified
using support vector machine,random forest and iterative random forest methods. The experimental results show
that the classification accuracy of iterative random forest is higher than that of support vector machine and random
forest method. This method provides a feasible and effective way to accurately identify epileptic EEG signals,and
has good application value.
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Knowledge Graph of Reservoir Structure Based on Thesaurus
YUAN Man, CHU Bing, XIAO Yao
Journal of Jilin University (Information Science Edition). 2020, 38 (1):  72-78. 
Abstract ( 359 )   PDF (608KB) ( 219 )  
In order to integrate reservoir structure knowledge by using knowledge graph technology and solve the
problem that in the process of ontology modeling,standards are rarely integrated into ontology model,and there
are some problems in the transformation process,such as coarse classification of concept granularity of descriptors
or no classification of descriptors at all,a new method of constructing knowledge graph based on thesaurus is
proposed,which is to mark the original thesaurus,establish mapping rules,construct transformation algorithm
and generate knowledge graph. The algorithm of annotation,mapping rule establishment and transformation
define the standardization process of building knowledge graph. Finally,some knowledge in the field of reservoir
structure is selected to construct the knowledge graph using the proposed method,which shows the feasibility of
the proposed method.
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Conjugated Search for Target Coordinate Location of Power Tunnel Robot
WANG Donghai, ZHOU Ping, ZHAO Xuan, CHU Qiang
Journal of Jilin University (Information Science Edition). 2020, 38 (1):  79-85. 
Abstract ( 268 )   PDF (1379KB) ( 131 )  
In the process of target coordinate location robot inspection at power tunnel environment,the
traditional search algorithm will produce information loss,which leads to the problem that the accuracy of search
and location results is not ideal. Therefore,a conjugate search algorithm is proposed. This algorithm builds the
robot inspection model,obtains the coordinate system which reflects the robot posture,and then sets up the robot
search path through the conjugate operator programming. The robot collects real-time inspection images along the
search path and identifies target nodes in the image according to the eigenvalues. The position and posture of the
robot in the process of inspection are calculated,and the results of hand-eye calibration and spatial coordinate
measurement are combined to realize the search and coordinate positioning of robot inspection targets.
Experimental results show that the proposed conjugate search algorithm is closer to 0 in positioning ERA
( Environmental Risk Assessment) index of target location,and has better positioning accuracy than traditional
search algorithm.
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Comfort Layout Method of Small Household Indoor Space#br# Based on Human Body Measurement#br#
CAO Junbo
Journal of Jilin University (Information Science Edition). 2020, 38 (1):  86-93. 
Abstract ( 363 )   PDF (1311KB) ( 333 )  
Existing small family indoor space human body comfort is not high,resulting in low utilization rate of
indoor space layout,poor complexity and other problems. Therefore,the body measurement technology is
introduced to design the comfortable layout method of small apartment interior space. The organizational structure
of indoor scene is defined based on the defined organizational structure of indoor scene. The indoor space layout
model is constructed by human measurement technology. Fuzzy theory is used to set the comfort index. Finally,
based on the obtained comfort evaluation index,the indoor scene elements are adapted to achieve the comfort
layout of small indoor space. The experimental results show that compared with the existing indoor space comfort
layout method for small houses,this method greatly improves the space utilization rate and complexity,which
fully indicates that the proposed method has better layout rationality.
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Design and Research of Rehabilitation Training System Based on Kinect
QIAN Chenghui, ZHANG Xinhao, TAO Jin, LIU Junlin
Journal of Jilin University (Information Science Edition). 2020, 38 (1):  92-98. 
Abstract ( 337 )   PDF (1000KB) ( 379 )  
In order to meet the rehabilitation training needs of patients with motor disfunction. A motion model to
extract human motion features is proposed based on the node Angle sequence based and the extraction of human
skeleton topology using Kinect equipment,and a rehabilitation training system based on Kinect motion sensor
camera is designed. The system collect human real-time skeleton data with Kinect,calculate the Angle between
feature nodes,and form the Angle sequence. The DTW ( Dynamic Time Warping) algorithm is used to compare
the similarity between the measured Angle sequence and the standard action sequence of the action library,judge
whether the action is standard and output the evaluation results. It is easy to use and costs less,which improves
the fun of the training process. The system realizes the rehabilitation training guidance for the patients with sports
disorder through human-machine interaction,and has a positive influence on the rehabilitation training process.
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Retrieval Model of Multi-Language Intelligent Information Based on BabelNet
YU Zaifu, YUAN Man
Journal of Jilin University (Information Science Edition). 2020, 38 (1):  99-106. 
Abstract ( 379 )   PDF (1384KB) ( 294 )  
Traditional cross-language information retrieval has problems such as low translation mapping
accuracy and semantic deviation after query expansion. To deal with this problem,a method of integrating
statistics and ontology is proposed to construct a multi-language information retrieval model. Using statistical
translation to solve the problem of translation mapping ambiguity,the multi-ontology BabelNet is used to
reduce the loss of semantic relevance. Because the ontology contains a large number of conceptual
connections,the ontology is used as the semantic layer representation to design the semantic weighting
algorithm. And it is built on the BM25F statistical information retrieval model as the user feedback sorting
algorithm. Finally,the multi-language information retrieval prototype system is designed according to the
established model,and the model is tested with the data set obtained based on the crawler technology. The
experimental results show that the average precision of the model is higher than the traditional machine
translation-based information retrieval model.
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Detection of Small Flow Based on Laser Self-Mixing Speckle Interference
YI Pengyu, WANG Shibo, ZHANG Zichao, MA Kai, SUN Hanwen, ZHANG Tingting, JIANG Chunlei
Journal of Jilin University (Information Science Edition). 2020, 38 (1):  107-110. 
Abstract ( 312 )   PDF (451KB) ( 165 )  
In order to realize the detection of small flow in laser self-mixing speckle interference,a self-mixing
speckle interference detection method based on laser is proposed. The method relies on the self-mixing speckle
interference signal obtained by the detection,the average frequency of the fluid is obtained by spectrum analysis
of the signal,and the average frequency is utilized. The line of fit with the flow rate further obtains the
relationship between the flow rate and the average frequency to accurately solve the small flow of the fluid.
Theoretical analysis and experiments show that the method can measure small flow easily and efficiently,and the
relative error of measurement is less than 1. 13%.
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