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Table of Content

    10 January 2017, Volume 37 Issue 1
    Design and implementation of resource management plane in virtual router platform
    GAO Xianming, WANG Baosheng, LI Tongbiao, XUE Huawei
    2017, 37(1):  1-5.  DOI: 10.11772/j.issn.1001-9081.2017.01.0001
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    Through researching and analyzing the management and maintenance problems in virtual router platform, a three-layer virtual router architecture including control plane, forwarding plane and resource management plane was put forward. Control plane and forwarding plane were two basic function planes in virtual router, correspondingly carrying logical control plane and logical forwarding plane. In order to achieve dynamic management of virtual router, resource management plane was introduced. Resource management plane was regarded as one important function plane, which was used to manage and configure virtual router. The structure and mechanism of resource management plane were mainly expressed, and a prototype system to support static installment and dynamic adjustment of router instances was carried out. The experimental results prove that the maximum processing ability of resource management plane is about 3205 strategies/second, and it can complete establishment of router instance within one second, which can meet management requirements of virtual router platforms.
    Virtual network embedding algorithm for dynamic virtual network requests
    YAUN Ying, WANG Cong, WANG Cuirong, SONG Xin, LYU Yanxia
    2017, 37(1):  6-11.  DOI: 10.11772/j.issn.1001-9081.2017.01.0006
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    Due to the dynamic characteristic of Virtual Network Request (VNR) resources, a Virtual Network Embedding algorithm based on Dynamic Virtual Network Requests (DVNR-VNE) was proposed. On the basis of mixed linear programming theory, we adopted multi-queue to pre-process different types of VNRs and established a multi-object embedding model with minimum mapping and migration cost. Those requests which need to release resource would be accepted firstly to support more VNRs, and the new arrived VNR would be embedded by an optimized WinDow-Virtual Network Embedding (WD-VNE) algorithm. The simulation results show that the proposed algorithm can reduce link cost, migration cost and can also obtain higher accept ratio.
    Heterogeneous scheduling algorithm with immediate successor finish time
    WANG Guan, WANG Yuxin, CHEN Xin, WANG Fei, GUO He
    2017, 37(1):  12-17.  DOI: 10.11772/j.issn.1001-9081.2017.01.0012
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    In the era of big data, data intensive computing always relies on distributed Heterogeneous Computing System (HCS), and an effective task scheduling method can improve the efficiency of a HCS. Based on a Directed Acyclic Graph (DAG) model, a task scheduling algorithm for heterogeneous computing named HSFT (Heterogeneous scheduling algorithm with immediate Successor Finish Time) was proposed. In the heterogeneous environment, especially when the computation cost and communication cost vary largely, the balance between them was considered and a more reasonable method was adopted, the product of the computation cost standard deviation and mean value was taken as the computation weight, and the ratio between the out degree communication cost weight and out degree was taken as the communication weight. Furthermore, based on the consideration of Earliest Finish Time (EFT), the immediate Successor Finish Time (SFT) was used for processor selection strategy. The experimental results on randomly generated DAGs show that the proposed algorithm performs better than HEFT (Heterogeneous Earliest Finish Time), SDBATS (Standard Deviation-Based Algorithm for Task Scheduling) and PEFT (Predict Earliest Finish Time) in terms of makespan, schedule length ratio, and efficiency without increasing time complexity.
    Multi-task assignment algorithm for mobile crowdsensing
    XU Zhe, LI Zhuo, CHEN Xin
    2017, 37(1):  18-23.  DOI: 10.11772/j.issn.1001-9081.2017.01.0018
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    Data transmission based on opportunistic communication in mobile crowdsensing may take a long period of time. To address this issue, a new Hub-based multi-Task Assignment (HTA) algorithm was proposed. In this algorithm, some nodes were selected to perform as the hubs which could help the requester node to deliver the tasks, according to the different characteristics of the social relationship of the nodes in mobile networks. When the task requester encountered a hub node, the hub node itself and its slave nodes were assigned tasks. After that, the hub node would distribute the tasks to the salve nodes, and received the results from them. Simulations were conducted on The ONE simulator. Compared with the oNline Task Assignment (NTA) algorithm, HTA algorithm reduced the time cost by 24.9% on average and improved the task completion ratio by 150% on average. The experimental results demonstrate that HTA algorithm can accelerate the accomplishment speed of the task and reduce the time cost.
    SaaS-oriented modeling and analysis of load balancing strategy
    MING Li, LI Tong, QIN Jianglong, ZHENG Ming, JIANG Xudong, XIE Zhongwen
    2017, 37(1):  24-30.  DOI: 10.11772/j.issn.1001-9081.2017.01.0024
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    To improve the efficiency of resource access in Software as a Service (SaaS) applications, a load balancing strategy combined with the important features of SaaS service was proposed. Firstly, the load balancing strategy was proposed by combining multi-tenancy and high scalability in SaaS service based on the distribution of request and global and local scalability. Secondly, the load balancing strategy model was constructed and simulated by a Petri net. Finally, this strategy was compared with Round Robin (RR), stochastic algorithm and Improved Least-Connection Scheduling (ILCS) load balancing algorithm in response time and throughput. The experimental results show that the response time and throughput of the proposed strategy become stable and they are superior to the other three algorithms after the request rate reaches 500 per second.
    Fast content distribution method of integrating P2P technology in cloud platform
    LIU Jing, ZHAO Wenju
    2017, 37(1):  31-36.  DOI: 10.11772/j.issn.1001-9081.2017.01.0031
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    The HyperText Transfer Protocol (HTTP) is usually adopted in the content distribution process for data transferring in cloud storage service. When large number of users request to download the same file from the cloud storage server in a short time, the cloud server bandwidth pressure becomes so large, and further the download becomes very slow. Aiming at this problem, the P2P technology was integrated into fast content distribution for cloud platform, and a dynamic protocol conversion mechanism was proposed to achieve fast and better content distribution process. Four protocol conversion metrics, including user type, service quality, time yield and bandwidth gains, were selected, and OpenStack cloud platform was utilized to realize the proposed protocol conversion method. Compared with the pure HTTP protocol or P2P downloading method, the experimental results show that the proposed method can guarantee client users obtaining less download time, and the bandwidth of service provider is saved effectively as there are many P2P clients.
    Accelerating parallel searching similar multiple patterns from data streams by using MapReduce
    FU Chen, ZHONG Cheng, YE Bo
    2017, 37(1):  37-41.  DOI: 10.11772/j.issn.1001-9081.2017.01.0037
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    The effective storage mode for time series was designed on Hadoop Distributed File System (HDFS), the sub-series were distributed to the compute nodes on Hadoop cluster by applying Distributed Cache tool, and the matrix of dynamic time warping distances was partitioned into several sub-matrixes. Based on MapReduce programming mode, by parallel computing sub-matrixes in each back-diagonal iteratively, the parallel computation of dynamic time warping distances was implemented, and an efficient parallel algorithm for searching similar patterns from data streams was developed by improving pruning redundant computation. The experimental results on the data set of snow depth long time series in China show that when the length of each time series is equal to or longer than 5000, the required time of parallel computing dynamic time warping distances is less than that of the corresponding sequential computation, and when the length of each time series is equal to or longer than 9000, the more the compute nodes used, the less the required parallel computation time; furthermore, when the length of each pattern is equal to or longer than 4000 and the number of compute nodes is equal to or larger than 5, the required time of parallel searching similar sub-series from data streams is 20% of the corresponding sequential searching time.
    Solution of two dimensional incompressible Navier-Stokes equation by parallel spectral finite element method
    HU Yuanyuan, XIE Jiang, ZHANG Wu
    2017, 37(1):  42-47.  DOI: 10.11772/j.issn.1001-9081.2017.01.0042
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    Due to a large number of computational grids and slow convergence existed in the numerical simulation of Navier-Stokes (N-S) equation, Triangular mesh Spectral Finite Element Method based on area coordinate (TSFEM) was proposed. And further, TSFEM was paralleled with OpenMP. Spectral method was combined with finite element method, and the exponential function with infinite smoothness was selected as the basis function to replace the polynomial function in the traditional finite element method, which can efficiently reduce the amount of computational grids as well as improve the convergence and accuracy of the proposed algorithm. Because area coordinates can facilitate the calculation of triangular units, which were selected as the computing units to enhance the applicability of the algorithm. The lid-driven cavity flow was used to verify the TSFEM. The experimental results show that, compared with the traditional Finite Element Method (FEM), the TSFEM greatly improves the convergence rate and the calculation efficiency.
    Provenance graph query method based on double layer index structure
    XU Guoyan, LUO Zhangxuan, SONG Jian, LYU Xin
    2017, 37(1):  48-53.  DOI: 10.11772/j.issn.1001-9081.2017.01.0048
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    To solve the problem of low query efficiency and high resource occupancy of the existing provenance graph query system, and consider the internal structure characteristics of provenance information, the relationship between the provenance of information and the data itself, a provenance graph query method based on double layer index structure was proposed. Firstly, for provenance graph query, a double layer index structure including global index based on dictionary table and local index based on bitmap was established. Global index was used to query the server nodes stored in provenance graph, and local index was for refining the query inside one server node. Secondly, based on the dual index structure, a provenance graph query method was designed, in view of the six kinds of selection index and three kinds of join link index. The experimental results show that the proposed method not only improves the query efficiency, but also reduces the waste of memory resources.
    Trajectory structure-based moving object hotspots discovery
    LYU Shaoqian, MENG Fanrong, YUAN Guan
    2017, 37(1):  54-59.  DOI: 10.11772/j.issn.1001-9081.2017.01.0054
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    Focused on the issue that the existing algorithms are unable to accurately detect active hotspots from trajectory data, a novel Trajectory Structure-based Hotspots discovery (TS_HS) algorithm was proposed. TS_HS consisted of the following two algorithms:Candidate Hotspots Discovery (CHSD) algorithm and Hotspots Filter (HSF) algorithm. First, trajectory dense regions were detected by the grid based clustering method CHSD as candidate hotspots. Second, the active hotspots region of moving objects were filtered by using HSF algorithm according to moving feature and time-varying characteristic of trajectories. The experiments on the Geolife dataset show that TS_HS is an effective solution for multi-density active hotspot problem, compared with Global Density threshold based Hot Region discovery (GD_HR) and Spatio-temporal Hot Spot Region Discovering (SDHSRD). The simulation results show that the proposed framework can detect active hotspots effectively based on the structure feature and time-varying characteristic of trajectory.
    Sweep coverage optimization algorithm for mobile sensor node with limited sensing
    SHEN Xianhao, LI Jun, NAI He
    2017, 37(1):  60-64.  DOI: 10.11772/j.issn.1001-9081.2017.01.0060
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    In the applications of mobile Wireless Sensor Network (WSN), since the sensing range of the sensor nodes is limited, the coverage analysis is a scan coverage problem for the target area. In this paper, a new scan coverage algorithm based on multi-objective optimization was proposed. In the target area, the double objective optimization strategy was used on path planning for a single mobile sensor node, which could maximize the coverage of the node and make scan coverage path to the shortest. Simulation experiments were carried out under the conditions with obstacles and without obstacles. Compared with the formation coverage algorithm for multiple nodes, the proposed algorithm can significantly reduce the mobile energy consumption while moderately reducing coverage rate.
    Moving target tracking scheme based on dynamic clustering
    BAO Wei, MAO Yingchi, WANG Longbao, CHEN Xiaoli
    2017, 37(1):  65-72.  DOI: 10.11772/j.issn.1001-9081.2017.01.0065
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    Focused on the issues of low accuracy, high energy consumption of target tracking network and short life cycle of network in Wireless Sensor Network (WSN), the moving target tracking technology based on dynamic clustering was proposed. Firstly, a Two-Ring Dynamic Clustering (TRDC) structure and the corresponding TRDC updating methods were proposed; secondly, based on centroid localization, considering energy of node, the Centroid Localization based on Power-Level (CLPL) algorithm was proposed; finally, in order to further reduce the energy consumption of the network, the CLPL algorithm was improved, and the random localization algorithm was proposed. The simulation results indicate that compared with static cluster, the life cycle of network increased by 22.73%; compared with acyclic cluster, the loss rate decreased by 40.79%; there was a little difference from Received Signal Strength Indicator (RSSI) algorithm in accuracy. The proposed technology can effectively ensure tracking accuracy and reduce energy consumption and loss rate.
    Named data networking based data dissemination mechanism for vehicular Ad Hoc network
    DENG Jian, DONG Baihong, CAO Hui, WU Lijuan, ZHANG Bo, WU Weigang
    2017, 37(1):  73-78.  DOI: 10.11772/j.issn.1001-9081.2017.01.0073
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    Vehicular Ad Hoc Network (VANET) is a highly dynamic communication network, which means it's a great challenge to design a stable data dissemination mechanism. Applying Named Data Networking (NDN), which focused on the content of the data, to VANET could effectively relive the problems brought by the frequent change of network topology. Firstly, the message types and data structure of NDN were improved. Secondly, the way of establishing routes according to section was put forward with the combination of characteristics of VANET so as to reduce the cost of data dissemination. The simulation results show that compared to the traditional NDN algorithm which is applied to VANET data dissemination, Average Hit Rate (AHR) and Average Forward Times (AFT) can be significantly improved by VANET data dissemination mechanism based on NDN. Therein, the average increase of AHR is about 53 percent points, while the average reduction of AFT is about 0.4 times. Therefore, the improved VANET data dissemination mechanism can improve the efficiency of data dissemination by using the new routing method.
    Data forwarding strategy based on weak state in vehicular Ad Hoc network
    HUANG Dan, HUANG Yan, HUAN Tian
    2017, 37(1):  79-83.  DOI: 10.11772/j.issn.1001-9081.2017.01.0079
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    To avoid the failure of data forwarding, brought by some characteristics of Vehicular Ad Hoc Networks (VANET), uniform distribution of vehicles, frequent network partition and mergence, etc., a new data delivery method based on Weak State Routing (WSR) from Traffic Control Center (TCC) to driving vehicles, called Weak State Forwarding (WSFD), was introduced in VANET. Firstly, a data packet collected by TCC was delivered to an Access Point (AP) along the direction of the destination vehicle. Secondly, the data packet was forwarded to the destination vehicle by AP within its communication range, at the same time, the location information of destination vehicle was carried by the data packet. Then, after comparing all the mapping information owned by the vehicle which received the data packet, the most deterministic map information was chosen by the vehicle and compared to the location information carried by the data packet so as to ensure the next forwarding direction. If the confidential level was quite high, the data packet was revised to move towards the mapping's corresponding central area, meanwhile, the information of destination vehicle carried by the data packet was updated. Otherwise, the original direction would be kept. Lastly, through several times' forwarding and revising, the data packet would be gradually approached to the area where the destination vehicle located, and the whole data delivery would be finally completed. Compared with Trajectory-based Statistical Forwarding for multihop infrastructure-to-vehicle data delivery (TSF) and Greedy Perimeter Stateless Routing (GPSR) algorithm, the WSFD algorithm could reduce the delivery delay to 5 seconds or less and elevate the delivery rate to 0.92 or more generally in the experiment of data transmission in 30 km*30 km square area. The experimental results show that the WSFD algorithm can improve safety of drivers and alleviate the traffic jam effectively.
    Data forwarding mechanism in software-defined vehicular Ad Hoc network
    YANG Zhiwei, CHEN Haoliang, ZHANG Bo, WU Lijuan, WU Weigang
    2017, 37(1):  84-89.  DOI: 10.11772/j.issn.1001-9081.2017.01.0084
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    Since the efficiency of data forwarding in Vehicular Ad Hoc Network (VANET) is low, a data forwarding mechanism in VANET based on Software-Defined Network (SDN) was proposed. Firstly, a hierarchical architecture of SDN based VANET was designed. This architecture was consist of local controller and vehicular, it could implement the separation of control and data forwarding, and also could achieve high scalability, reliability and efficiency. Secondly, a new data forwarding mechanism was proposed, which used dynamic programming and binary search. Finally, compared with Ad Hoc On-demand Distance Vector routing (AODV), Destination Sequenced Distance Vector routing (DSDV), Dynamic Source Routing (DSR) and Optimized Link State Routing (OLSR) algorithm, the proposed algorithm could improve packet delivery fraction and end-to-end delay. Therein, the average increase of packet delivery fraction was about 100%, while the average reduction of end-to-end delay was about 20%. The simulation results show that the data forwarding mechanism in software-defined VANET can effectively improve the packet delivery and reduce the end-to-end delay.
    Low power mapping based on improved genetic algorithm with Prim initial population selection for 3D network-on-chip
    SONG Guozhi, WANG Cheng, TU Yao, ZHANG Dakun
    2017, 37(1):  90-96.  DOI: 10.11772/j.issn.1001-9081.2017.01.0090
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    To solve the problem of properly mapping the computational task onto a three-dimensional Network-on-Chip (NoC), an improved algorithm based on Genetic Algorithm (GA) was proposed. GA has the fast random searching ability and Prim algorithm can get the minimal spanning tree of a weighted connected graph. By combining the two algorithms' advantages, the improved algorithm could properly assign computational tasks onto each network node, achieving a high efficiency on solving network power consumption and heat problems. The simulation experiments were carried out to compare the proposed improved GA based on Prim algorithm with GA based 3D NoC mapping algorithm. The simulation results indicate that the average power consumption of the improved GA based on Prim algorithm is lower:from the overall trend, the reduction on power consumption is positive correlated to the increase of the number of processing units, and when there are 101 processing units, the average power consumption is 32% lower than that of the traditional GA.
    Antenna down-tilt angle self-optimization method based on particle swarm in long term evolution network
    LIAN Xiaocan, ZHANG Pengyuan, TAN Guoping, LI Yueheng
    2017, 37(1):  97-102.  DOI: 10.11772/j.issn.1001-9081.2017.01.0097
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    To solve the coverage and capacity optimization problem of Self-Organizing Network (SON) in the 3rd Generation Partnership Project (3GPP), an active antenna down-tilt angle optimization method based on Particle Swarm Optimization (PSO) algorithm was proposed. First, the number of User Equipments (UE) served by evolved Node B (eNB) was determined, and the Reference Signal Received Power (RSRP) and position measured from the UE were obtained. Second, the Spectral Efficiency (SE) was regarded as the fitness function which defined by optimization goals. Then, down-tilt angle optimization was regarded as multidimensional optimization problem, and antenna down-tilt angle was regarded as the set of particles to resolve the optimal value by the PSO algorithm. Finally, the capacity and coverage self-optimization of Long Term Evolution (LTE) networks was achieved by adjusting down-tilt angle independently. By simulations, different objective functions made different optimization results. When the average spectrum efficiency was set as the optimization goal, the spectral efficiency of traditional golden section algorithm increased by 12.9% than primary settings, the spectral efficiency of PSO was increased by 22.5%. When the weighted average spectral efficiency was set as the optimization goal, the spectral efficiency of golden section algorithm was not significantly improved but that of PSO was increased by 19.3% for edge users. The experimental results show that the proposed method improves cell throughput and system performance.
    Quasi-optimal period computation model for hierarchical checkpoint protocol
    LYU Hongwu, GU Lei, WANG Huiqiang, ZOU Shichen, FENG Guangsheng
    2017, 37(1):  103-107.  DOI: 10.11772/j.issn.1001-9081.2017.01.0103
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    With the increase of High Performance Computation (HPC) system scale, it's very important to increase the efficiency of the checkpoint. A model to compute the quasi-optimal period for hierarchical checkpoint protocol was proposed. First, the execution of an application in HPC system was assessed, and checkpoint period optimization problem was abstracted as the nonlinear checkpoint cost model. Second, the hierarchical checkpoint cost formula was derived by simulating the possible fault location; two deceleration parameters and an acceleration parameter were introduced to reflect the impact of message logging on the hierarchical checkpoint. The simulation results show that, compared with the quasi-optimal period checkpoint cost, the average error value of the proposed model is below 5%, which is 20% less than that of the traditional model based on Markov chain. The proposed model can signally increase the efficiency of the hierarchical checkpoint protocol; meanwhile enhance the availability of the HPC system.
    Test case prioritization based on discrete particle swarm optimization algorithm
    ZHANG Weixiang, QI Yuhua, LI Dezhi
    2017, 37(1):  108-113.  DOI: 10.11772/j.issn.1001-9081.2017.01.0108
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    With the ability to improve regression testing efficiency, test case prioritization has become a hot topic in software testing research. Since test case prioritization based on requirement is usually inefficient, a test case prioritization method based on discrete particle swarm optimization and test-point coverage, called Discrete Particle Swarm Optimization for Test Case Prioritization (TCP-DPSO) was proposed. Firstly, the various factors affecting prioritization were divided into two categories:Cost-Keys and Win-Keys, and then general test average yield index by normalizing was obtained. Then, particle's position and velocity were defined by use of switcher and basic switching sequence, the mutation operator was introduced by referencing mutation strategy of Genetic Algorithm (GA), and the exploration and development capabilities were adjusted by adopting variable inertia weight, which could promote sustainable evolution and approach optimization goals. The experimental results show that TCP-DPSO is similar to GA and dramatically better than random testing on optimal solution quality and it is superior to GA on success rate and average computing time, which indicates its better algorithm stability.
    Real-time online evaluation method of helper thread prefetching quality
    ZHANG Jianxun, GU Zhimin
    2017, 37(1):  114-119.  DOI: 10.11772/j.issn.1001-9081.2017.01.0114
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    Focusing on the multifarious and time-consuming optimization process of traditional helper thread parameter value enumeration method, a real-time online helper thread prefetching quality assessment method was proposed. First, the help thread prefetching Quality of Service (QoS) target was defined. Second, the dynamic evaluation index of helper thread prefetching quality was analyzed, as well the helper thread prefetching QoS model. Finally, a dynamic and adaptive helper thread prefetching adjustment algorithm was presented. The algorithm was based on phase behavior and dynamic prefetching benefit information to determine the suitable degree of parameter values, and whether to need feedback optimization, so as to realize the adaptive adjustment and control of helper thread prefetching. By applying the adaptive prefeching algorithm, the speed up of Mst's hotspot module was 1.496. The experimental results show that the proposed adaptive prefetching evaluation method can control parameter values adaptively according to the dynamic phase behavior and prefetching benefit information.
    Software protection method based on monitoring attack threats
    TANG Zhanyong, LI Zhen, ZHANG Cong, GONG Xiaoqing, FANG Dingyi
    2017, 37(1):  120-127.  DOI: 10.11772/j.issn.1001-9081.2017.01.0120
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    To increase the difficulty of software reverse analysis and improve software security, a software protection method based on monitoring attack threats was proposed. By deploying the threat-monitoring net, a variety of threats in software execution process could be real-time detected and resolved, so that the software is in a relatively safe environment and difficult to be reversely analyzed. There are three main research aspects in this protection scheme:1) Attack threat description. The potential attack threats were analyzed and then they were described with a triple . 2) Deployment of threat-monitoring net. The node base was constructed after analyzing the feature of each threat and designing the corresponding detection methods. The reasonable deployment scheme based on characteristics of nodes was selected, and these nodes were deployed effectively into different places of software. 3) Prototype system implementation and experimental design. According to the idea of this protection scheme, a prototype system was implemented, and a group of test cases was protected with the system to collect the experimental data. The evaluation on the aspects of performance consumption and security was made. The final result shows the proposed method is feasible and effective to protect software.
    Application-layer DDoS defense model based on Web behavior trajectory
    LIU Zeyu, XIA Yang, ZHANG Yilong, REN Yuan
    2017, 37(1):  128-133.  DOI: 10.11772/j.issn.1001-9081.2017.01.0128
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    To defense application-layer Distributed Denial of Service (DDoS) built on the normal network layer, a defense model based on Web behavior trajectory in the Web application server was constructed. User's access behavior was abstracted into Web behavior trajectory, and according to the generation approach about attack request as well as behavior characteristics of user access to Web pages, four kinds of suspicion were defined, including access dependency suspicion, behavior rate suspicion, trajectory similarity suspicion, and trajectory deviation suspicion. The deviation values between normal sessions and attack sessions were calculated to detect the application-layer DDoS to a specific website. The defense model prohibited the user access from DDoS when detecting the attack request generated by the user. In the experiment, real data was acted as the training set. Then, through simulating different kinds of attack request, the defense model could identify the attack request and take the defense mechanism against the attack. The experimental results demonstrate that the model can detect and defense the application-layer DDoS to a specific website.
    Early warning method for driving safety based on CUDA
    ZHAO Yongtao, CHEN Qingkui, FANG Yuling, ZHAO Deyu, JI Lina
    2017, 37(1):  134-137.  DOI: 10.11772/j.issn.1001-9081.2017.01.0134
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    To improve the safety of vehicles while driving, a computer vision-based inter-vehicle distance estimation and warning method was proposed in this paper. First, shadow detection method was applied to detect shadow of cars ahead, and inter-vehicle distance estimation function was built based on the distance between shadow and vision center of a frame. Then, estimation equations for non-threatened background optical flow was built, and by judging optical flow with the estimation equations, the abnormal objects could be separated from others, thus the overtaking event could be recognized. Based on the inter-vehicle distance and detection of overtaking event, the driver could be timely warned of the potential safety hazard. The experimental results prove that the proposed method can estimate inter-vehicle distance and detect overtaking event accurately. Finally, NVIDIA GeForce GTX680 GPU (Graphic Processing Unit) was used to accelerate the algorithm on Compute Unified Device Architecture (CUDA) platform and achieve the processing speed of 48.9 ms per frame which basically meets the real-time processing demand.
    M-TAEDA: temporal abnormal event detection algorithm for multivariate time-series data of water quality
    MAO Yingchi, QI Hai, JIE Qing, WANG Longbao
    2017, 37(1):  138-144.  DOI: 10.11772/j.issn.1001-9081.2017.01.0138
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    The real-time time-series data of multiple water parameters are acquired via the water sensor networks deployed in the water supply network. The accurate and efficient detection and warning of pollution events to prevent pollution from spreading is one of the most important issues when the pollution occurs. In order to comprehensively evaluate the abnormal event detection to reduce the detection deviation, a Temproal Abnormal Event Detection Algorithm for Multivariate time series data (M-TAEDA) was proposed. In M-TAEDA, it could analyze the time-series data of multiple parameters with BP (Back Propagation) model to determine the possible outliers, respectively. M-TAEDA algorithm could detect the potential pollution events through Bayesian sequential analysis to estimate the probability of an abnormal event. Finally, it can make decision through the multiple event probability fusion in the water supply systems. The experimental results indicate that the proposed M-TAEDA algorithm can get the 90% accuracy with BP model and improve the rate of detection about 40% and reduce the false alarm rate about 45% compared with the temporal abnormal event detection of Single-Variate Temproal Abnormal Event Detection Algorithm (S-TAEDA).
    Real-time crowd counting method from video stream based on GPU
    JI Lina, CHEN Qingkui, CHEN Yuanjing, ZHAO Deyu, FANG Yuling, ZHAO Yongtao
    2017, 37(1):  145-152.  DOI: 10.11772/j.issn.1001-9081.2017.01.0145
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    Focusing on low counting accuracy caused by serious occlusions and abrupt illumination variations, a new real-time statistical method based on Gaussian Mixture Model (GMM) and Scale-Invariant Feature Transform (SIFT) features for video crowd counting was proposed. Firstly, the moving crowd were detected by using GMM-based motion segment method, and then the Gray Level Co Occurrence Matrix (GLCM) and morphological operations were applied to remove small moving objects of background and the dense noise in non-crowd foreground. Considering the high time-complexity of GMM algorithm, a novel parallel model with higher efficiency was proposed. Secondly, the SIFT feature points were acted as the basis of crowd statistics, and the execution time was reduced by using feature exaction based on binary image. Finally, a novel statistical analysis method based on crowd features and crowd number was proposed. The data sets with different level of crowd number were chosen to train and get the average feature number of a single person, and the pedestrians with different densities were counted in the experiment. The algorithm was accelerated by using multi-stream processors on Graphics Processing Unit (GPU) and the analysis about efficiently scheduling the tasks on Compute Unified Device Architecture (CUDA) streams in practical applications was conducted. The experimental results indicate that the speed is increased by 31.5% compared with single stream, by 71.8% compared with CPU.
    Stream computing system for monitoring copy plate vehicles
    QIAO Tong, ZHAO Zhuofeng, DING Weilong
    2017, 37(1):  153-158.  DOI: 10.11772/j.issn.1001-9081.2017.01.0153
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    The screening of the copy plate vehicles has timeliness, and the existing detection approaches for copy plate vehicles have slow response and low efficiency. In order to improve the real-time response ability, a new parallel detection approach, called stream computing, based on real-time Automatic Number Plate Recognition (ANPR) data stream, was proposed. To deal with the traffic information of the road on time, and plate vehicles could be timely feedback and controlled, a stream calculation model was implemented by using the threshold table of road travel time and the time sliding window, which could access real-time traffic data stream to calculate copy plate vehicles. On the platform of Storm, this system was designed and implemented. The calculation model was verified on a real-time data stream which was simulated by the real ANPR dataset of a city. The experimental results prove that a piece of license plate recognition data can be dealt with in milliseconds from the time of arrival to the calculation completion, also, the calculation accuracy is 98.7%. Finally, a display system for copy vehicles was developed based on this calculation model, which can show the copy plate vehicles from the road network on the current moment.
    Design of remote wireless monitoring system for smart home based on Internet of things
    DENG Yun, LI Chaoqing, CHEN Xiaohui
    2017, 37(1):  159-165.  DOI: 10.11772/j.issn.1001-9081.2017.01.0159
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    Based on ARM920T kernel S3C2440, embedded Web services, QT technology and wireless networking technology, a smart home monitoring system was designed. The system was composed of a host of smart home, ZigBee/Wi-Fi wireless sensor control network and smart home client software. The hardware and software design of the host of smart home was completed:the embedded Linux operating system was transplanted in the ARM platform; the embedded Web services were established by using gSOAP tool; USB to serial driver and Wi-Fi wireless LAN (Local Area Network) driver were configured; ZigBee wireless sensor control network was formed, the program design of the coordinator node and terminal node was completed, the data communication protocol was made, and the client program was designed by using QT technology. Finally, the tests of establishment of ZigBee network, terminal nodes joining the network and sensor node data transmission were done. The test results show that the sensor nodes in the network can transmit the detection information to the coordinator, and the smart home client software can complete the remote monitoring and control of home environment through the host of smart home.
    Near field communication-enabled water meter system with mobile payment
    ZHANG Chengyu, WANG Rangding, YAO Ling, FU Songyin, ZUO Fuqiang, GAO Qifei, JIANG Ming
    2017, 37(1):  166-169.  DOI: 10.11772/j.issn.1001-9081.2017.01.0166
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    In view of the problems of traditional prepaid meters such as inefficiency and inconvenience, a Near Field Communication (NFC)-enabled water meter system that has the functions of mobile payment and data query was proposed. Firstly, according to the business requirements of the prepaid water meter, the overall architecture of the water meter system was developed based on NFC technology, and the software and hardware were designed. Secondly, a low-power mechanism which was used to wake up the water meter by detecting the external magnetic field changes was proposed. Finally, the security performance in mobile payment of the water meter system was analyzed based on NFC security protocols. The experimental results show that users can dynamically awake the water meter system, and utilize the functions of mobile payment, data querying and data uploading, by using the NFC mobile phones or other mobile terminals with NFC module.
    Smart wireless water meter reading system for multi-story residential buildings
    FU Songyin, WANG Rangding, YAO Ling, ZHANG Chengyu, SHAN Guanmin, HU Guowei
    2017, 37(1):  170-174.  DOI: 10.11772/j.issn.1001-9081.2017.01.0170
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    Smart Wireless Water Meter Reading System (SWWMRS) built on the conventional Wireless Sensor Network (WSN) platform can not meet the requirements of low cost, low power consumption, high efficiency and high reliability in practice. In this work, a novel SWWMRS for typical multi-story buildings was proposed. Based on the feature of the SWWMRS and deployment environment as well as the business logic, an improved algorithm for all neighbor discoveries was proposed to achieve automatic networking and centralized routing management. At the meter reading stage, a minimum global forward strategy with a minimum residual energy nodes avoidance strategy were adopted to balance the energy consumption between nodes. Additionally, the mechanism to avoid confliction in Media Access Control (MAC) layer and the low power idle listening strategy were optimized. The testing results for the proposed system in a 24-story residential building show that the system performance of communication distance, power consumption and reliability can meet the needs of the practical applications. Meanwhile, compared with CC2530 scheme, better performance in communication distance, meter reading success rate, efficiency and power consumption can be achieved.
    Analysis algorithm of electroencephalogram signals for epilepsy diagnosis based on power spectral density and limited penetrable visibility graph
    WANG Ruofan, LIU Jing, WANG Jiang, YU Haitao, CAO Yibin
    2017, 37(1):  175-182.  DOI: 10.11772/j.issn.1001-9081.2017.01.0175
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    Focused on poor robustness to noise of the Visibility Graph (VG) algorithm, an improved Limited Penetrable Visibility Graph (LPVG) algorithm was proposed. LPVG algorithm could map time series into networks by connecting the points of time series which satisfy the certain conditions based on the visibility criterion and the limited penetrable distance. Firstly, the performance of LPVG algorithm was analyzed. Secondly, LPVG algorithm was combined with Power Spectrum Density (PSD) to apply to the automatic identification of epileptic ElectroEncephaloGram (EEG) before, during and after the seizure. Finally, the characteristic parameters of the LPVG network in the three states were extracted to study the influence of epilepsy seizures on the network topology. The simulation results show that compared with VG and Horizontal Visibility Graph (HVG), although LPVG had a high time complexity, it had strong robustness to noise in the signal:when mapping the typical periodic, random, fractal and chaos time series into networks by LPVG, it was found that as the noise intensity increased, the fluctuation rates of clustering coefficient by LPVG network were always the lowest, respectively 6.73%, 0.05%, 0.99% and 3.20%. By the PSD and LPVG analysis, it was found that epilepsy seizure had great influence on the brain energy. PSD was obviously enhanced in the delta frequency band, and significantly reduced in the theta frequency band; the topological structure of the LPVG network changed during the seizure, characterized by the independent enhanced network module, increased average path length and decreased graph index complexity. The PSD and LPVG applied in this paper could be taken as an effective measure to characterize the abnormality of the energy distribution and topological structure of single EEG signal channel, which would provide help for the pathological study and clinical diagnosis of epilepsy.
    Dynamic sampling method for wireless sensor network based on compressive sensing
    SONG Yang, HUANG Zhiqing, ZHANG Yanxin, LI Mengjia
    2017, 37(1):  183-187.  DOI: 10.11772/j.issn.1001-9081.2017.01.0183
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    It is hard to obtain a satisfactory reconstructive quality while compressing time-varying signals monitored by Wireless Sensor Network (WSN) using Compressive Sensing (CS), therefore a novel dynamic sampling method based on data prediction and sampling rate feedback control was proposed. Firstly, the sink node acquired the changing trend by analyzing the liner degree differences between current reconstructed data and last reconstructed data. Then the sink node calculated the suitable sampling rate according to the changing trend and fed back the result to sensors to dynamically adjust their sampling process. The experimental results show that the proposed dynamic sampling method can acquire higher reconstructed data accuracy than the CS data gathering method based on static sampling rate for WSN.
    Survey on construction of measurement matrices in compressive sensing
    WANG Qiang, ZHANG Peilin, WANG Huaiguang, YANG Wangcan, CHEN Yanlong
    2017, 37(1):  188-196.  DOI: 10.11772/j.issn.1001-9081.2017.01.0188
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    The construction of measurement matrix in compressive sensing varies widely and is on the development constantly. In order to sort out the research results and acquire the development trend of measurement matrix, the process of measurement matrix construction was introduced systematically. Firstly, compared with the traditional signal acquisition theory, the advantages of high resource utilization and small storage space were expounded. Secondly, on the basis of the framework of compressive sensing and focusing on four aspects:the construction principle, the generation method, the structure design of measurement matrix and the optimal method, the construction of measurement matrix in compressive sensing was summarized, and advantages of different principles, generations and structures were introduced in detail. Finally, based on the research results, the development directions of measurement matrix were prospected.
    Block-sparse adaptive filtering algorithm based on inverse hyperbolic sine function against impulsive interference
    WEI Dandan, ZHOU Yi, SHI Liming, LIU Hongqing
    2017, 37(1):  197-199.  DOI: 10.11772/j.issn.1001-9081.2017.01.0197
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    Since the existing block-sparse system identification algorithm based on Mean Square Error (MSE) shows poor performance under impulsive interference, an Improved Block Sparse-Normalization Least Mean Square (IBS-NLMS) algorithm was proposed by introducing the inverse hyperbolic sine cost function instead of MSE. A new cost function was constructed and the additive value was obtained by steepest-descent method. Furthermore, a new vector updating equation for filter coefficients was deduced. The adaptive update of the weight vector was close to zero in the presence of impulsive interference, which eliminated the estimation error of adaptive updating based on the wrong information. Meanwhile, mean convergence behavior was analyzed theoretically and then the simulation results demonstrate that in comparison with the Block Sparse-Normalization Least Mean Square (BS-NLMS) algorithm, the proposed algorithm has higher convergence rate and less steady-state error under non-Gaussion noise impulsive interference and abrupt change.
    Information hiding method based on Have messages in BitTorrent protocol
    GAO Bin, ZHAI Jiangtao, DAI Yuewei
    2017, 37(1):  200-205.  DOI: 10.11772/j.issn.1001-9081.2017.01.0200
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    Concerning the problem in concealment performance and embedding capacity of the existing information hiding algorithm based on BitTorrent (BT) protocol messages, a new information hiding method based on Have message queues was proposed. Firstly, the capacity analysis module was set to judge whether the embedding capacity was beyond the limit of hiding capacity before embedding. Secondly, the improved parity mapping information coding method was used when the secret information was embedded into the sort of Have queues. Finally, the cyclical redundancy check method was introduced to verify whether the secret information was transmitted correctly when extracting. The experimental results show that, compared with the original parity mapping information coding method, the proposed method is able to increase the embedding capacity and improve the hiding performance of covert channel for its little influence on the statistical characteristics of Have queues.
    Spam detection model of campus network based on incremental learning algorithm
    CHEN Bin, DONG Yizhou, MAO Mingrong
    2017, 37(1):  206-211.  DOI: 10.11772/j.issn.1001-9081.2017.01.0206
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    Concerning the problem brought by a large number of spam, an incremental passive attack learning algorithm was proposed. The passive attack learning method was based on the Simple Mail Transfer Protocol (SMTP) session log initiated by the email host in the campus during half a year. Analysis on the status of delivery rate and many types of failure message of the host behavior in the session record was conducted, and the effective adaptation was ultimately achieved by detecting spam source host behavior on the recent email classification. The experimental results show that after implementing several rounds of classification strategy adjustment, the detection accuracy of the proposed model can reach 94.7%. The design is very useful to effectively detect internal spam host and control the spam from the source.
    Time-based strategy audit scheme of access control list in multi-layer firewall
    WANG Xudong, CHEN Qingping, LI Wen, ZHANG Xinming
    2017, 37(1):  212-216.  DOI: 10.11772/j.issn.1001-9081.2017.01.0212
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    To solve the Access Control List (ACL) strategic audit problem in multi-layer firewalls, the policy anomalies in single firewall and between multi-layer firewalls were analyzed based on time. Then the Anomaly Detection based on Backtracking Algorithm (ADBA) was proposed by constructing the tree structure according to the topology of firewalls. First, the ACL policy of each firewall was analyzed and the data format was unified to the database. Second, the tree structure of firewall was built based on the topology of the firewall and the anomaly would be detected in a single firewall. Finally, the data in the database and the tree structure was used in ADBA to detect and record the abnormal strategy. The experimental results show that compared with the Semi-isomorphic Marked Firewall Decision Diagram (SMFDD) algorithm, the proposed ADBA can reduce the execution time of anomaly detection by 28.01% and reduce the miscalculation of anomaly detection according to the time factor. The ADBA can be implemented effectively at multi-layer firewalls ACL audit to improve detection accuracy and reduce detection time.
    Hybrid algorithm for identifying error signatures in hierarchical identity based cryptography batch verification
    XU Guoyu, WANG Yingfeng, MA Xiaofei, WANG Kefeng, YAN Ruoyu
    2017, 37(1):  217-221.  DOI: 10.11772/j.issn.1001-9081.2017.01.0217
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    Focusing on the issue of identifying error signatures in Hierarchical Identity Based Cryptography (HIBC) batch verification, a hybrid algorithm of identifying the error signatures was proposed. Firstly, a balanced binary tree was built which used all signatures as the leaves. Secondly, divide-and-conquer and exponent testing methods were used to find error signatures. Meanwhile, the relevance of temporary computing values was used to reduce computing cost. The performance analyses show that the proposed algorithm costs less computation than the individual, the generalized binary splitting, the exponential and the triple pruning search algorithms when there are more than two error signatures. The proposed algorithm can effectively identify error signatures in HIBC batch verification and can be applied in cloud computing authentication.
    Incremental formation of concept lattice based on intent waned value
    WU Jie, LIANG Yan, MA Yuan
    2017, 37(1):  222-227.  DOI: 10.11772/j.issn.1001-9081.2017.01.0222
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    Concerning the tedious process during the construction of concept lattice, to improve the efficiency of building concept lattices, a new incremental method of constructing concept lattice based on intent waned value by seeking top elements was proposed. Firstly, top element, original concept, produced concept, new concept, producer concept, the set of intent waned values, reminded parent concept, superset delete and regular queue were formally defined; the judging theorem and proof whether the concept elements were top elements were given. Secondly, the elements were extracted from the regular queue of the original lattice in due order and the reminded parent concepts were got after superset delete. Finally, the top elements were found from the equivalent classes of the reminded parent concepts and the regular queue of the new lattice was gradually generated. Time complexity was effectively reduced compared with the Attribute-based Concept Lattice Incremental Formation (CLIF-A) algorithm and the FastAddIntent algorithm by theory analysis. In comparison with simulated experiments, the time consumption of the proposed algorithm was far less than the comparative approaches in large size of population. The simulation results show that the proposed algorithm is simple, and can effectively improve the time performance, meanwhile provides better performance in construction efficiency.
    Automatic image annotation method using multi-label learning convolutional neural network
    GAO Yaodong, HOU Lingyan, YANG Dali
    2017, 37(1):  228-232.  DOI: 10.11772/j.issn.1001-9081.2017.01.0228
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    Focusing on the shortcoming of the automatic image annotation, the lack of information caused by artificially selecting features, convolutional neural network was used to learn the characteristics of samples. Firstly, in order to adapt to the characteristics of multi label learning of automatic image annotation and increase the recall rate of the low frequency words, the loss function of convolutional neural network was improved and a Convolutional Neural Network of Multi-Label Learning (CNN-MLL) model was constructed. Secondly, the correlation between the image annotation words was used to improve the output of the network model. Compared with other traditional methods on the Technical Committee 12 of the International Association for Pattern Recognition (IAPR TC-12) benchmark image annotation database, the experimental result show that the Convolutional Neural Network using Mean Square Error function (CNN-MSE) method achieves the average recall rate of 12.9% more than the Support Vector Machine (SVM) method, the average accuracy of 37.9% more than the Back Propagation Neural Network (BPNN) method. And the average accuracy rate and average recall rate of marked results improved CNN-MLL method is 23% and 20% higher than those of the traditional CNN. The results show that the marked results improved CNN-MLL method can effectively avoid the information loss caused by the artificially selecting features, and increase the recall rate of the low frequency words.
    Agriculture-related product name extraction and category labeling based on ontology and conditional random field
    HUANG Nian'e, HUANG He, WANG Rujing
    2017, 37(1):  233-238.  DOI: 10.11772/j.issn.1001-9081.2017.01.0233
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    Traditional information extraction method based on Conditional Random Field (CRF) requires large-scale labeled corpus, it is expensive to label corpus manually and the extraction precision is low in processing agriculture-related product name extraction and category labeling. In order to solve this problem, a method of agriculture-related product name extraction and category labeling based on agricultural ontology and CRF was proposed, automatic extraction and classification of agriculture-related product names was regarded as sequence labeling. Firstly, original data was processed, word, part of speech, geographical attributes and ontology concept features were selected. Then, parameters of the CRF model were trained by the improved quasi-Newton algorithm and decoding was implemented by Viterbi algorithm. A total of four groups of comparative experiments were completed and seven categories were identified. CRF, Hidden Markov Model (HMM) and Maximum Entropy Markov Model (MEMM) were compared through experiments. Finally, the supply and demand trend analysis of agriculture produce was accomplished. The experimental results show that the overall precision, recall and F-score of the open test were increased by 10.20%, 59.78% and 37.17% respectively by adding ontology concepts with appropriate CRF features; it also proves the feasibility, effectiveness and practical significance of the method in promoting automatic supply and demand docking of agricultural products.
    Application of chaos cuckoo search algorithm in harmonic estimation
    NIU Haifan, SONG Weiping, NING Aiping, MA Yiyuan
    2017, 37(1):  239-243.  DOI: 10.11772/j.issn.1001-9081.2017.01.0239
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    Concerning slow convergence speed in the later stage, low calculation accuracy and easily falling into the local optimum of basic Cuckoo Search (CS) algorithm, a Cuckoo Search based on Chaos theory (CCS) algorithm was proposed. Firstly, the chaos initialization was used to increase population diversity. Secondly, the chaos disturbance operator was introduced to the local optimal value to jump out of the premature convergence and improve the calculation accuracy. Finally, the global optimization was improved. Four single objective benchmark functions were tested. The simulation results in the best, the worst, average, median and standard deviation value show that CCS algorithm has faster convergence speed and higher convergence precision than CS algorithm. Harmonic is the vital cause of the distortion of current waveform and voltage instability. The analysis of harmonics in power quality analysis is a very important part in power system. The CCS algorithm was applied to harmonic estimation. The experimental results show that the CCS algorithm has better performance compared with the Particle Swarm Optimization (PSO) according to the analysis of harmonic current in mean value and standard deviation.
    Polarimetric synthetic aperture radar feature analysis and classification based on multi-layer support vector machine classifier
    SONG Chao, XU Xin, GUI Rong, XIE Xinfang, XU Feng
    2017, 37(1):  244-250.  DOI: 10.11772/j.issn.1001-9081.2017.01.0244
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    In order to make full use of the ability of of Synthetic Aperture Radar (SAR) images with different polarization features for characterizing different types of ground objects, an analysis and classification approach of polarimetric SAR feature based on multi-layer Support Vector Machine (SVM) classifier was proposed. Firstly, the optimal feature subsets suitable for different terrain types were determined through the feature analysis. Then, the method of hierarchical classification tree was used for SVM classification step by step according to the feature subset of each object type.Finally, the overall final result was obtained. The experimental results of RadarSAT-2 polarimetric SAR image classification show that, the classification accuracy of the proposed approach is approximately 85% for four kinds of ground objects such as water area, cultivated land, forest land and urban area and the overall classification accuracy is up to 86%. The proposed approach can make full use of the characteristics of the different ground object target types, improve the classification accuracy and reduce the time complexity.
    Gesture segmentation and positioning based on improved depth information
    LIN Haibo, WANG Shengbin, ZHANG Yi
    2017, 37(1):  251-254.  DOI: 10.11772/j.issn.1001-9081.2017.01.0251
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    Aiming at the problem that segmented gesture by Kinect depth information usually contains wrist data, which easily causes subsequent false gesture recognition, a gesture segmentation and positioning algorithm based on improved depth information was proposed. Firstly, the gesture binary image was detected based on depth information threshold limit in experimental space. Secondly, according to characteristics of common gestures, accurate gesture was segmented by gesture endpoint detection and variable threshold algorithm. In order to obtain stable segmentation results, morphological processing of segmented gesture was conducted. Lastly, the gesture positioning algorithm was proposed based on the method of combining gesture gravity center coordinates and maximum inscribed circle center coordinates. The experimental results show that the proposed gesture segmentation method has better accuracy and stability than the existing algorithm. The combined gesture positioning is more stable than gesture gravity center positioning and skeletal data positioning of Kinect Software Development Kit (SDK) and it has no singular points.
    Object recognition method based on RGB-D image kernel descriptor
    LUO Jian, JIANG Min
    2017, 37(1):  255-261.  DOI: 10.11772/j.issn.1001-9081.2017.01.0255
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    The traditional RGB-Depth (RGB-D) image object recognition methods have some drawbacks, such as insufficient feature learning and poor robustness of feature coding. In order to solve these problems, an object recognition method of RGB-D image based on Kernel Descriptor and Locality-constrained Linear Coding (KD-LLC) was proposed. Firstly, based on the kernel function of image block matching, several complementary kernel descriptors from RGB-D images, such as 3D shape, size, edges and color, were extracted using Kernel Principal Component Analysis (KPCA). Then, the extracted feature from different cues, were processed by using LLC and Spatial Pyramid Pooling (SPP) to form the corresponding image coding vectors. Finally, the vectors were combined to obtain robust and distinguishable image representation. As a hand-crafted feature method, the proposed algorithm was compared to other hand-crafted feature methods on a RGB-D image dataset. In the proposed algorithm, multiple cues from depth image and RGB image were used, and the sampling points selection and basis vectors calculation schema for depth kernel descriptor generation were proposed. Due to above-mentioned improvements, the category and instance recognition accuracy of the proposed algorithm for objects can respectively reach 86.8% and 92.7%, which are higher than those of the previously hand-crafted feature methods for object recognition from RGB-D images.
    3D face modeling and validation in cross-pose face matching
    LI Xinxin, GONG Xun
    2017, 37(1):  262-267.  DOI: 10.11772/j.issn.1001-9081.2017.01.0262
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    Since the existing 3D face acquisition technology has many restrictions on gathering scene, a 3D face reconstruction technology based on several images was proposed, and its validation was verified. First, an iterative computing model of pose and depth value estimation was proposed to implement the accurate estimation of feature depth. Then the depth values integration based on several images and shape modeling were further investigated. Finally, the Iterative Pose and Depth Optimization (IPDO) algorithm was compared with Nonlinear Least-Squares Model with Symmetry and Regularization terms (NLS1_SR) on Bosphorus database, the modeling precision was improved by 9%, and the projected image of 3D model is similar to the 2D inputted image. The experimental results show that under the condition of big pose change, the proposed recognition algorithm assisted by 3D information can improve the recognition rate of more than 50%.
    Single image in-depth dehazing algorithm based on optimization of guided image
    DONG Yufei, YANG Yan, CAO Biting
    2017, 37(1):  268-272.  DOI: 10.11772/j.issn.1001-9081.2017.01.0268
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    Aiming at the quality loss problems such as degradation in contrast and color distortion of image captured in haze and fog weather conditions, a single image in-depth dehazing algorithm based on optimization of the guided image was proposed. The local mean and standard deviation of the image were adopted to optimize the guided image on the basis of analysing the character of atmospheric veil. Then, the guided image was further filtered by using the dual zone filtering to get smooth and sharp-edged guided image. The atmospheric veil was estimated through the fast guided filtering. At last, a clear image would be recovered based on the atmospheric scattering physical model. The experimental results show that the recovered image is clear and natural, and rich in details. Its close view is dehazed completely, while the dehazing of its distant view is improved greatly. The proposed algorithm achieves good results where the depth of the field has a sudden saltation and improved the visibility and robustness of outdoor vision system.
    Improved ellipse fitting algorithm based on Letts criterion
    CAO Junli, LI Jufeng
    2017, 37(1):  273-277.  DOI: 10.11772/j.issn.1001-9081.2017.01.0273
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    The commonly used Least Square (LS) ellipse fitting algorithm based on minimum algebraic distance is simple and easy to implement, but it has no choice to the sample points, which leads to the fitting results are easily inaccurate due to the error points. According to this case, an improved ellipse fitting algorithm based on Letts criterion was proposed to overcome the shortage of LS algorithm. Firstly, the ellipse was fitted from the fitting curve by using the LS ellipse fitting algorithm based on minimum algebraic distance. Then, the algebraic distance of ellipse fitted by LS algorithm from the point distance on the fitting curve was set as the fitting point set. After the point set was verified to be normal distribution, the points which were greater than|3σ|were determined to be outliers and eliminated by using Letts criterion. Then the steps above were repeated until all points were within the scope of [-3σ,]. Finally, the best fitting ellipse was obtained. The simulation experiment results show that the fitting error of the improved algorithm based on Letts criterion is within 1.0%, and its fitting accuracy is improved by at least 2 percentage points compared with the LS algorithm under the same condition. The simulation result and the practical application in roundness measurement of cigarette verify the effectiveness of the improved algorithm.
    Knowledge driven automatic annotating algorithm for game strategies
    CHEN Huanhuan, CHEN Xiaohong, RUAN Tong, GAO Daqi, WANG Haofen
    2017, 37(1):  278-283.  DOI: 10.11772/j.issn.1001-9081.2017.01.0278
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    To help users to quickly retrieve the interesting game strategies, a knowledge driven automatic annotating algorithm for game strategies was proposed. In the proposed algorithm, the game domain knowledge base was built automatically by fusing multiple sites that provide information for each game. By using the game domain vocabulary discovering algorithm and decision tree classification model, game terms of the game strategies were extracted. Since most terms existing in the strategies in the form of abbreviation, the game terms were finally linked to knowledge base to generate the full name semantic tags for them. The experimental results on many games show that the precision of the proposed game strategy annotating method is as high as 90%. Moreover, the game domain vocabulary discovering algorithm has a better result compared with the n-gram language model.
    Real-time traffic accident prediction based on AdaBoost classifier
    ZHANG Jun, HU Zhenbo, ZHU Xinshan
    2017, 37(1):  284-288.  DOI: 10.11772/j.issn.1001-9081.2017.01.0284
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    The traditional road traffic accident forecast mainly uses the historical data, including the number and the loss of traffic accidents, to predict the future trend, however, the traditional method can not reflect the relationship between the traffic accident and real-time traffic characteristics, and it also can not prevent accidents effectively. In order to solve the problems above, a real-time traffic accident prediction method based on AdaBoost classifier was proposed. Firstly, the road traffic states were divided into normal conditions and dangerous conditions, and the real-time collection of traffic flow data was used as the characteristic variable to characterize the different states, so the real-time prediction problem could be converted to a classification problem. Secondly, the Probability Density Function (PDF) of traffic flow characteristics under the two conditions in different time scales were estimated by Parzen window nonparametric estimation method, and the estimated density function was analyzed by the separability criterion based on probability distribution, then the sample data with appropriate characteristic variable and time scale could be determined. Finally, the AdaBoost classifier was trained to classify different traffic conditions. The experimental results show that the correct ratio by using standard deviation of traffic flow characteristics to classify test samples is 7.9% higher than that by using average value. The former can reflect the differences of different traffic states better, and also get better classification results.
    Ultra wideband indoor localization based on inner triangle centroid algorithm
    WEI Pei, JIANG Ping, HE Jingjing, ZHANG Huimeng
    2017, 37(1):  289-293.  DOI: 10.11772/j.issn.1001-9081.2017.01.0289
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    Aiming at the poor flexibility of Automated Guided Vehicle (AGV) localization method in industrial working field, an Ultra WideBand (UWB) indoor localization system by using DW1000 Radio Frequency (RF) chip was designed and implemented. Firstly, to solve the problem of conflicts and networking of tags, the efficient mechanisms for multi-station ranging and multi-tag scheduling were proposed. Secondly, concerning the low accuracy and poor stability of the triangle centroid localization algorithm caused by maximal ranging errors, a concept of credibility was introduced and the inner triangle centroid algorithm was proposed, which could weaken the influence of maximal ranging errors through credibility coefficient to improve the algorithm performance. Finally, the proposed system was applied to the industrial workshop with 20 tags. For a single tag, the average frequency of coordinate updating reached 24 Hz and its standard deviation was 3 Hz; the static average localization error was 11.7 cm and its standard deviation was 2.5 cm; the dynamic maximum error was within 30 cm. The experimental results show that the proposed localization system has the characteristics of high real-time performance, high precision and high stability.
    Design of iterative learning controller for systems with random noise
    XIA Hao, ZHANG Lijie
    2017, 37(1):  294-298.  DOI: 10.11772/j.issn.1001-9081.2017.01.0294
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    To reduce the negative impact of stochastic noise in iterative learning control system, an iterative learning controller design method based on the Infinite Impulse Response (ⅡR) digital filter was proposed. For the first batch, the output errors from two repeated experiments were filtered by wavelet transform. Then the input/output data during the wavelet filtering process were used to obtain an equivalent ⅡR filter, which would be used to reconstruct the error objective function and optimize the iterative learning controller. Finally, the obtained ⅡR filter was applied to filter out the stochastic noise from subsequent batches until the convergence condition was met. Through simulation, compared with wavelet filtering, it could be demonstrated that by applying the proposed method, the 2-norm of output error could be reduced by nearly 15% and the ringing caused by setting the wavelet filter threshold too small was also avoided. The cumulative noise between the batches could be reduced by about 9%. The simulation results show that the proposed algorithm not only significantly reduces the negative effect of stochastic noise, but also effectively improves the accuracy of the tracking system.
    Dynamic estimation about service time of flight support based on Bayesian network
    XING Zhiwei, TANG Yunxiao, LUO Qian
    2017, 37(1):  299-304.  DOI: 10.11772/j.issn.1001-9081.2017.01.0299
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    Concerning the problems of estimating the service time of airport flight support, and the particularity, complexity, and influence factors' uncertainty of flight support service process, an estimation model of flight support service time based on Bayesian Network (BN) was proposed. The knowledge of aviation experts and the machine learning of historical data were combined by the proposed model, and the incremental learning characteristic of BN was used to adjust the BN model dynamically, so as to make itself adapt to new conditions and constantly update the service time estimates of flight support. By using the data selected from a large domestic hub airport information system, the proposed BN model was trained via the Expectation Maximization (EM) algorithm to obtain the test results. The analysis of experimental results and model evaluation show that the proposed method can effectively estimate the service time of flight support and has higher accuracy. In addition, the sensitivity analysis demonstrates that the flight density during flight arrival time has the strongest influence on flight support service time.
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