Current Issue

• • 2020, Vol. 43 No. 5 Published date:28 October 2020 Last issue Next issue

  REVIEW

  • Electronic Warfare Confrontation between the United States and Russia
  • LU Zhen, HUANG Yong-hua
  • Journal of Beijing University of Posts and Telecommunications. 2020, 43(5): 1-8. DOI:10.13190/j.jbupt.2020-037
  • Abstract ( 1093 )     HTML( 1987 )     　　
  • Electronic warfare and electromagnetic spectrum warfare play a deterrent role in modern warfare, which is the primary factor determining the victory or failure of a war. It has been fully confirmed in the recent series of local wars and conflicts. Since the end of the cold war, the United States and Russia have launched fierce competition and confrontation in this field, and new types of electronic warfare weapons emerge in endlessly. It is summarized that current situation and trend of the development of electronic warfare weapons in the two countries in recent years, which can be used as a reference for the development of electronic warfare weapons in China.
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  PAPERS

  • Urban Short-Term Traffic Flow Prediction Algorithm Based on CNN-ResNet-LSTM Model
  • PU Yue-yi, WANG Wen-han, ZHU Qiang, CHEN Peng-peng
  • Journal of Beijing University of Posts and Telecommunications. 2020, 43(5): 9-14. DOI:10.13190/j.jbupt.2019-243
  • Abstract ( 1840 )     HTML( 1078 )     　　
  • With the continuous advancement of smart city construction, urban short-term traffic flow forecasting becomes more and more important. According to the influence of traffic flow characteristics and external factors on traffic flow forecast results, the model CNN-ResNet-LSTM for urban short-term traffic flow forecasting is proposed. The model integrates convolutional neural networks(CNN), residual neural units(ResNet) and long-short-term memory networks(LSTM) into an end-to-end network framework. The convolutional neural network is used to capture the local spatial characteristics of traffic flow, and multiple residual neural units are added to deepen the network depth and improve the prediction accuracy. On the other hand, the long short-term memory-cycle neural network is used to capture temporal characteristics of traffic flow data. The output results of the two networks are combined by the corresponding weights to obtain the predicted results through the trajectory data. Finally, the traffic flow prediction values of the urban areas are obtained by fusing with external factors. Through the verification of the CNN-ResNet-LSTM model by Beijing data, the model is not only higher in accuracy than the traditional model, but also has fewer parameters in the case of ensuring the accuracy of prediction.
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  • Energy Efficiency Optimization Scheme Based on Energy Harvesting in Mobile Edge Computing
  • XUE Jian-bin, LIU Xing-xing, DING Xue-qian
  • Journal of Beijing University of Posts and Telecommunications. 2020, 43(5): 15-20. DOI:10.13190/j.jbupt.2019-249
  • Abstract ( 596 )     HTML( 758 )     　　
  • Aiming at low energy efficiency and single energy service of resource-constrained terminal devices caused by intensive computing tasks offloading in mobile edge computing (MEC), a system energy efficiency optimization scheme based on energy harvesting is proposed. Firstly, the energy harvesting status and power allocation of users are analyzed under the constraints of offloading transmission power and so on, and a joint optimization model is established to maximize system energy efficiency. Secondly, the offloading energy efficiency is transformed into standard convex optimization by the generalized fractional programming theory, and the objective function is iteratively optimized by setting the Lagrange function to obtain the optimal energy indicator variable and power allocation. Simulations show that the proposed scheme can effectively improve the energy efficiency of users in MEC system, and guarantee the quality of service (QoS) of users, achieve the green communication simultaneously.
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  • Analysis and Improvement of Semi-Supervised K-means Clustering Based on Particle Swarm Optimization Algorithm
  • SUN Yi, XIA Qi-zhao
  • Journal of Beijing University of Posts and Telecommunications. 2020, 43(5): 21-26. DOI:10.13190/j.jbupt.2020-017
  • Abstract ( 691 )     HTML( 521 )     　　
  • Traditional particle swarm optimization has obvious advantages, but with increased complexity of the environment. When the traditional algorithm is used, the sensitivity of the clustering center is increased, there are too many empty clusters, and the performance of the class label has insufficient influence on the clustering results. An improved algorithm is proposed, which aims at semi-supervised K-means clustering; first, the clustering center is initialized by random calculation in an adaptive K-value method, and the particles are encoded according to the needs of the mean clustering algorithm. At the same time, the objective function is reconstructed with the concept of soft constraints, and finally the improved algorithm is used for optimization. The adaptive parameters in the improved particle swarm optimization algorithm is improved, two disturbance methods of immune disturbance and chaos disturbance is introduced, and the annealing strategy and dynamic clustering strategy at the same time is applied. Experiments show that the algorithm has solved the above problem.
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  • A Hybrid Forwarding Information Base for Multi-Modal Data
  • WANG Bin-zhi, LI Zhuo, LUO Peng, MA Tian-xiang, LIU Kai-hua
  • Journal of Beijing University of Posts and Telecommunications. 2020, 43(5): 27-33. DOI:10.13190/j.jbupt.2020-084
  • Abstract ( 492 )     HTML( 551 )     　　
  • In order to solve the problems of rapid indexing, efficient storage of forwarding information and longest prefix matching brought by multi-modal data in the forwarding information base(FIB) in the future network, a hybrid FIB based on neural networks, called Hybrid-FIB, which supports multi-modal data indexing is designed. Hybrid-FIB differentiates different type of data to obtain input vectors for neural network model, and then trains a neural network hybrid index model that can achieve uniform distribution. Experiments show that deploying two sets of Hybrid-FIB on the static random access memory can not only achieve the longest prefix matching of the multi-modal data, but also have better retrieval speed and misjudgment rate than the current network.
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  • Optimization of Mobile Manipulator Sorting Path Based on Improved Genetic Algorithm
  • WANG Huai-jiang, LIU Xiao-ping, WANG Gang, HAN Song
  • Journal of Beijing University of Posts and Telecommunications. 2020, 43(5): 34-40. DOI:10.13190/j.jbupt.2020-069
  • Abstract ( 641 )     HTML( 1152 )     　　
  • Path planning is the core technology of mobile manipulators. Aiming at the problem of the traditional fixed-station mobile manipulator planning algorithms, a method for optimizing the picking path of mobile manipulators based on improved genetic algorithm is proposed. By analyzing the position of items to be picked, a sorting path model of mobile manipulators at a single station and a traveling salesman problem(TSP) model for multiple stations are established. An improved genetic algorithm is used to optimize the station coordinates in the workspace, which planned the shortest path grasped by the mobile robot arm and moved between multi-station points. Experiments show that, by using the improved hierarchical evolution selection operator and the optimal nearest neighbor crossover operator, compared with the traditional genetic algorithm, the convergence speed is increased by 46.15%, the path is shortened by 45.99%, and the system running time is reduced by 25.80%. That improved system efficiency.
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  • A Subset Selection Strategy on Multiple-Radar Anti-Jamming Systems
  • NIE Zhao, LIU Jie-yi, ZHANG Ming-yang, LI Hao
  • Journal of Beijing University of Posts and Telecommunications. 2020, 43(5): 41-47. DOI:10.13190/j.jbupt.2020-063
  • Abstract ( 438 )     HTML( 495 )     　　
  • For the false target identification method based on joint estimation of parameters, the discrimination of false target probability can be improved by increasing the number of radar stations. However, the excessive increased radar stations will cause a serious waste of equipment resources. For this problem, a gradual shrinkage subset selection strategy on multiple-radar anti-jamming systems is proposed. Aiming to existing radar stations, the rapid shrinkage method and the global shrinkage method are considered to select some transmitting and receiving stations to form the radar subset which guarantee the preset false target discrimination performance. All of the selecting stations have better spatial distribution or stronger discrimination ability in the system. Compared with exhaustive search, the proposed subset selection strategy has a great reduction in computational complexity. Simulation shows that the radar subset can maintain the similar discrimination performance with the original multiple-radar systems. At the same time, it optimizes the number of radar stations, reduces the amount of data processed by the fusion center and the required communication links, which effectively save the operating cost.
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  • Few-Shot Face Spoofing Detection Using Feedforward Learning Network
  • SONG Yu, SUN Wen-yun, CHEN Chang-sheng
  • Journal of Beijing University of Posts and Telecommunications. 2020, 43(5): 48-56. DOI:10.13190/j.jbupt.2020-068
  • Abstract ( 475 )     HTML( 705 )     　　
  • In order to overcome the limitations of the existing face spoofing detection methods under few-shot face anti-spoofing applications, this paper proposes to use feedforward learning network for face anti-spoofing. The convolutional filters are learned unsupervisedly from the images in a feedforward manner. The feedforward learning network is adapted in the spoof face detection applications by using face anti-spoofing task-oriented convolutional filters learned from the training images. The eigenvectors that correspond to the smallest eigenvalues obtained from the principle component analysis transform are used as convolution filters for extracting features from images. The method is evaluated on some benchmark datasets including CASIA-FASD dataset, Idiap Replay-Attack dataset and OULU-NPU dataset. Experiments show that under the cross presentation attack detection experiments, the proposed method significantly improves the classification accuracy of existing methods.
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  • Opportunistic Transmission Control Method for Apron Sensing Network Based on Game Theory
  • CHEN Wei-xing, SU Jing-fang, ZHAO Hui
  • Journal of Beijing University of Posts and Telecommunications. 2020, 43(5): 57-63. DOI:10.13190/j.jbupt.2020-029
  • Abstract ( 481 )     HTML( 402 )     　　
  • The low delivery rate and large network overhead is of great challenge in the apron-aware network scenario. To solve the problem, an opportunistic transmission control method (OTCM) based on game theory is proposed. First, combining the characteristics of the scene to establish the initial-pass node game model to achieve message transmission sequencing and the problem of transmission priority is solved. Then a game rule system and function is planned that integrates multi-dimensional topology node attributes to achieve the optimization of the message transmission environment and the reliability of the transmission process. At the same time, according to the utility function in the game system, the topology can be adaptively updated after adding the new opportunity node. Hence the topology maintains its optimal state. Using opportunistic network environment to establish a domestic apron scene simulation. Verification shows that the OTCM algorithm has an average delivery rate of 52.50% and an average transmission delay reduced to 1 773 s compared with other opportunistic routing strategies, which basically meets the research goals.
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  • A Method of Detecting Sleep Apnea Using Random Forest
  • Lü Xing-feng, LI Jin-bao
  • Journal of Beijing University of Posts and Telecommunications. 2020, 43(5): 64-70. DOI:10.13190/j.jbupt.2019-255
  • Abstract ( 574 )     HTML( 687 )     　　
  • To solve the problem that various respiratory signals in polysomnography make the detection of sleep apnea complicated and affect patients' sleep, a method of automatic sleep apnea detection using random forest is proposed. The energy and marginal spectrum distribution of sleep apnea is significantly different from that of normal sleep after Hilbert-Huang transform. By extracting the relevant frequency domain features, combining with the time domain features, the random forest method in machine learning method is used to detect sleep apnea, which effectively reduces the detection complexity and improve the accuracy. Experiments show that this method is more convenient and accurate than the existing method, more suitable for home environment,and has a wide range of application prospects.
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  • Distant Supervision Relation Extraction Method Based on Highway Multi-Kernel Network
  • LI Wei, CHEN Shu-dong, OUYANG Xiao-ye, DU Rong, WANG Rong
  • Journal of Beijing University of Posts and Telecommunications. 2020, 43(5): 71-76. DOI:10.13190/j.jbupt.2020-071
  • Abstract ( 432 )     HTML( 370 )     　　
  • As a technology that can quickly generate large amounts of labeled data, the distant supervision is increasingly used in relation extraction. However, there are still problems such as insufficient text feature extraction and noise in the bag. A distant supervision relation extraction method based on highway multi-kernel network is proposed to solve these questions. Firstly, the feature of sentences are deeply extracted by highway network and multi-kernel convolution; and then the intra-bag attention mechanism is used to improve the sentence weight of the correct annotation in bag and reduce the intra-bag noise to obtain the bag‘s embedding. Subsequently, the inter-bag attention mechanism is used to reduce the inter-bag noise for each group of bags with the same relation to obtain the group‘s embedding. Finally, groups are used as training samples to train the classifier to achieve relation extraction. Experiment shows that this method has better relation extraction performance than existing methods.
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  • A Social Relationship Direction Gating Algorithm for Graph Convolutional Networks
  • LI Lei, XIE Yang, JIANG Ya-fei, LIU Yong-bin
  • Journal of Beijing University of Posts and Telecommunications. 2020, 43(5): 77-83. DOI:10.13190/j.jbupt.2020-074
  • Abstract ( 501 )     HTML( 699 )     　　
  • Facing the problem in social user attitude analysis that the original social relationship direction between users in social networks hinders the flow of attitude information and label propagation, a social relationship direction gating algorithm for graph convolutional networks is proposed. The algorithm first performs graph convolution on the origin and reverse social relationship directions to obtain two types of user node attitude feature vectors, and then leverages the gating mechanism to integrate the feature vectors dynamically. While expanding the propagation of attitude information, the algorithm can also capture the differences of user influence to automatically select the flow of attitude information. Experiments on two real hot topic datasets show that the accuracy of the existing graph convolutional networks can be effectively improved after adding the proposed algorithm.
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  • Data Augmentation for Chinese Clinical Named Entity Recognition
  • WANG Peng-hui, LI Ming-zheng, LI Si
  • Journal of Beijing University of Posts and Telecommunications. 2020, 43(5): 84-90. DOI:10.13190/j.jbupt.2020-032
  • Abstract ( 832 )     HTML( 1528 )     　　
  • Chinese clinical named entity recognition plays an important role in recognizing medical entities contained in Chinese electronic medical records. Limited to lack of large annotated data, most of existing methods concentrate on employing external resources to improve the performance of clinical named entity recognition, which require lots of time and efficient rules. To solve the problem of lack of large annotated data, data augmentation using sequence adversarial generative network is used to generate more various data depending on entities and non-entities in the training set. Experiments show that when using generated data to expand training set, the proposed named entity recognition system has achieved competitive performance compared with state-of-art methods, which shows the effectiveness of our data augmentation method.
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  • Dynamic Gesture Recognition Based on Characteristics of Encoded Video Data
  • XIE Xiao-yan, ZHAO Huan, JIANG Lin
  • Journal of Beijing University of Posts and Telecommunications. 2020, 43(5): 91-97. DOI:10.13190/j.jbupt.2020-056
  • Abstract ( 563 )     HTML( 800 )     　　
  • Aiming at the challenges to scene adaptability and computational complexity of dynamic gesture recognition, a method based on characteristics of encoded video data is proposed. Firstly, density-based spatial clustering of applications with noise is used to extract motion trend features from motion vectors. Then, the motion trends are classified by random forest. Finally, combined by the hand shape features extracted by convolutional neural network(CNN), the dynamic gesture is recognized. The experiment shows that the proposed method has an average recognition rate of 94.22% and 94.48% respective for university of Cambridge and Northwestern University hand gesture data sets. Compared with the scheme combine of CNN and long short-term memory, the gesture recognition time is reduced by 85%. It can still maintain a higher recognition rate for the complex background with insufficient illumination, represents a higher robustness.
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  • Commodity Classification of Online Based on High-Level Feature Fusion
  • LIU Yi-chen, SUN Hua-zhi, MA Chun-mei, JIANG Li-fen, ZHONG Chang-hong
  • Journal of Beijing University of Posts and Telecommunications. 2020, 43(5): 98-104,117. DOI:10.13190/j.jbupt.2020-033
  • Abstract ( 416 )     HTML( 523 )     　　
  • In order to realize automatic classification of commodities by leveraging text titles of commodities, a commodity classification model high-level feature fusion (HFF) based on high-level feature fusion is proposed. Firstly, a char embedding and word embedding based low-level feature representation method for the text title is proposed. Then a stronger feature expression of the commodity title structure can be obtained. Secondly, a joint self-attention mechanism, convolutional neural network, and channel attention are proposed to enhance the low-level features and obtain high-level enhancement features of the text title. Finally,by fusing the high-level enhancement features of the word embedding and the char embedding of the text, a comprehensive feature of the text title of the commodity is finally obtained and used for the commodity classification. Experiments are conduct on the dataset of the commodity titles. The experiments show that the classification accuracy of HFF for the third-level commodity can reach 84.348%. In addition, the recall and the F1 value of the HFF reach 47.8% and 49.4%, respectively, which is superior to the existing advanced short text classification method that can be used for the commodity text titles classification.
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  • A Human Action Counting and Recognition Method Based on CSI
  • LIU Xi-wen, CHEN Hai-ming
  • Journal of Beijing University of Posts and Telecommunications. 2020, 43(5): 105-111. DOI:10.13190/j.jbupt.2020-040
  • Abstract ( 1094 )     HTML( 1285 )     　　
  • Nowadays WiFi channel state information is widely applied in passive(unobtrusive)human continuous activity recognition. The article uses commercial off-the-shelf devices and proposes a human action counting and recognition (Wi-ACR) method, based on channel state information(CSI). Wi-ACR takes advantage of the threshold algorithm and action indicator to detect the start and end time of a set of continuous actions,and then counts the number of actions through the peak-find algorithm and determines the start and end time of each action. After that,Wi-ACR takes the waveform-feature-based action recognition model and the statistical-feature-based action recognition model to obtain action recognition results respectively. Experiments show that Wi-ACR can achieve action counting accuracy of 95% and recognition accuracy of 90% with these two recognition models,in the scenarios with two types of actions(i.e. squat and walk)occurring simultaneously.
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  REPORTS

  • An Abnormal Sound Recognition Method Based on EEMD
  • WEI Juan, GU Xing-quan, NING Fang-li
  • Journal of Beijing University of Posts and Telecommunications. 2020, 43(5): 112-117. DOI:10.13190/j.jbupt.2020-075
  • Abstract ( 480 )     HTML( 727 )     　　
  • In order to optimize the efficiency of combined features in abnormal sound recognition,an algorithm for detecting the effectiveness of abnormal sound frame signals and extracting multi-layer features using ensemble empirical mode decomposition (EEMD) is proposed. Firstly,an ensemble empirical mode decomposition is performed on the abnormal sound frame signal to obtain the intrinsic model function,and then the validity of the frame signal is tested according to the given layer threshold of the intrinsic modal function. Finally,the Mel frequency cepstral coefficients,the inverted Mel frequency cepstral coefficients,the linear prediction cepstral coefficients,the short-time energy and energy ratio are extracted for each layer of the intrinsic modal function of the effective frame signal,and then all of them are normalized and spliced into multi-layer feature. According to the extracted features,the deep convolutional neural network is used to realize the classification and recognition of abnormal sound. Simulations show that the proposed new method can achieve a recognition rate of 98.65% in four types of abnormal sound recognition.
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  • Reverse-Analysis of S-Box for SM4-Like Algorithms Based on Side Channel Technology
  • MA Xiang-liang, LI Bing, YANG Dan, HUANG Ke-zhen, DUAN Xiao-yi
  • Journal of Beijing University of Posts and Telecommunications. 2020, 43(5): 118-124. DOI:10.13190/j.jbupt.2020-034
  • Abstract ( 701 )     HTML( 781 )     　　
  • In the profiled scenario, the common method of reverse analysis is the template attack based on multi-Gaussian distribution. The article applies the concept of deep learning to the field of reverse analysis for the first time under the same conditions, and proposes an S-box reverse analysis algorithm based on deep learning. By selecting the deep learning algorithm, loss function and label design method suitable for side channel reverse analysis, an S-box reverse recovery experiment is conducted for SM4-like algorithm. It is shown that it is feasible to employ deep learning method to carry out S-box reverse analysis, which can have better performance comparing to template attack under certain circumstances. Moreover, the predicting effect of multi-layer perception algorithm surpasses that of convolutional neural network algorithm.
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  • Wireless Localization Algorithm of Adaptive Levy Whale Based on Mapping Curve
  • YU Xiu-wu, LI Ying, LIU Yong, XIAO Ren-rong, YU Hao
  • Journal of Beijing University of Posts and Telecommunications. 2020, 43(5): 125-129,136. DOI:10.13190/j.jbupt.2019-192
  • Abstract ( 567 )     HTML( 410 )     　　
  • Aiming at the problems of low calculation efficiency and low positioning accuracy of the multidimensional scaling map (MDS-MAP) algorithm, a wireless localization algorithm of adaptive Levy whale based on mapping curve (AWL-MC) is proposed. The mapping curve distance analysis method is used to make rough relative positioning of the localization nodes, so as to improve the calculation efficiency of nodes. Then the relative coordinates are converted into absolute coordinates by linear transformation. Finally, the adaptive Levy flight whale optimization algorithm is adopted to perform global and local search optimization processing for the coordinates of positioning nodes, so as to avoid local optimal solution and improve positioning accuracy. Simulations show that compared with MDS-MAP, AWL-MC algorithm has a 66.42% improvement rate in positioning accuracy and a 52.57% improvement in calculation efficiency. Compared with the multidimensional scaling extended Kalman filter, AWL-MC algorithm has a 57.80% improvement rate in positioning accuracy and a 66.01% improvement in calculation efficiency.
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  • Maximum Fairness Power Allocation Scheme in Downlink Multi-Cell NOMA Systems
  • TIAN Xin-ji, JIANG Qing-li
  • Journal of Beijing University of Posts and Telecommunications. 2020, 43(5): 130-136. DOI:10.13190/j.jbupt.2020-050
  • Abstract ( 600 )     HTML( 836 )     　　
  • A power allocation scheme to maximize fairness is proposed for multi-cell downlink non-orthogonal multiple access (NOMA) systems with multi-cluster multi-user. Firstly, the power allocation optimization problem for maximizing fairness is constructed. Then, the relationship between the total power of a signal cluster and the users‘ rate in the cluster is derived under the criterion of maximum fairness, and based on this conclusion, the power allocation among users in the optimization problem is transformed into that among clusters. Finally, an inter cluster power allocation algorithm based on iterative is presented. This algorithm calculates the user's rate under the maximum fairness criterion by assigning average power for each cluster, and obtains the optimal powers by adjusting the cluster power of the cell with the maximum rate and the cluster power of the cell with the minimum rate. Simulations show that the proposed scheme outperform the existing schemes in terms of fairness.
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  • Routing Algorithm Based on Density Clustering for Delay Tolerant Network
  • WEN Wei
  • Journal of Beijing University of Posts and Telecommunications. 2020, 43(5): 137-142. DOI:10.13190/j.jbupt.2020-018
  • Abstract ( 414 )     HTML( 383 )     　　
  • In order to overcome the problem that there are too many message copies and large data transmission delay in the delay tolerant network, the author optimizes the Prophet routing protocol based on historical prediction, and propose a routing protocol based on density clustering. The algorithm adopts cluster analysis theory and birth and death process theory, the density cluster is constructed and maintained accurately, so that the message copies in the network can be controlled in real time. On this basis, the random linear network coding strategy based on Q-learning is proposed. The value function estimation method in the enhanced learning domain is adopted to obtain the linear independent coding packets efficiently through the intermediate nodes, so as to improve the coding efficiency of the network. Simulations show that this algorithm can obtain a higher message delivery rate in comparison with epidemic and Prophet routing algorithms, the data transmission delay can be well controlled under the condition of sufficient buffer thereafter. The algorithm has strong dynamic adaptability to the delay tolerant network.
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  • Single Channel Interference Signal Reconstruction and Suppression Algorithm in Low Altitude Small Target Detection
  • SHI Xiao-zhu, DU Yang-fan, LI Xiao-hui, FANG Cong
  • Journal of Beijing University of Posts and Telecommunications. 2020, 43(5): 143-148. DOI:10.13190/j.jbupt.2020-008
  • Abstract ( 498 )     HTML( 600 )     　　
  • In order to solve the problem that the traditional passive detection algorithm cannot achieve the reference signal reception under a single receiving channel when using mobile communication systems to detect low-altitude small targets, a direct wave suppression algorithm based on interference signal reconstruction was proposed for the detection of low-altitude small targets. Firstly, the mobile communication system is used to perceive the channel transmission environment. On this basis, the interference signals such as direct wave signal and multipath signal are reconstructed by using the time-frequency characteristics of orthogonal frequency division multiplexing. Finally, the direct wave interference in the single receiving channel is suppressed by the extended cancellation algorithm. The simulation results show that the proposed method can improve the echo detection performance and improve the accuracy of low-altitude small target detection in the case of single receiving channel of mobile communication system.
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