Computer and Modernization

High-speed Train Communication Network Simulation Based on Industrial Ethernet

ZHANG Zhi-guo

2017, 0(9): 1-6. doi:10.3969/j.issn.1006-2475.2017.09.001

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In order to solve the problems of instantaneity, low retardance and reliability, which related to the train Ethernet correspondence system, this thesis studies the present development of railway transmission network and
analyzes the main factors influencing Ethernet according to the standard IEC 61375-2-5 and IEC 61375-3-4. Basing on the present research, this thesis presents a scheduling algorithm based on the high instantaneity and low retardance of industry Ethernet, which can also be used for train correspondence system. The simulation research shows that the performance of this optimization scheduling algorithm is 10 times better than the traditional queue data transmission algorithm.

Mobile-terminal-oriented File Transfer Method in Dynamic Network Environment

YAN A-bin1,2, WANG Jing1,2

2017, 0(9): 7-12,73. doi:10.3969/j.issn.1006-2475.2017.09.002

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Nowadays, the schemes of file transfer between smart-phones can be divided into two categories, Internet-based and Ad-hoc-network-based. When the clients connect to the Internet, they can use P2P direct technology or a file server to forward files. Otherwise, WiFi hotspots or Bluetooth technologies are used to set up Ad-hoc networks between clients to realize file transfers. But in the case of network environment changing, the switch between the two schemes will lead to retransmission of files, and the parts that have already been transferred will be wasted. To solve this problem, this article puts forward a file transfer scheme which can adapt to dynamic network environments, choose the optimized route for users, and improve the speed of file transmission.
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Network Monitoring and Management Mechanism in Mobile Routing System

CHEN Hao-fei， DONG Ping， ZHENG Tao

2017, 0(9): 13-18. doi:10.3969/j.issn.1006-2475.2017.09.003

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The mobile routing system based on wireless multi link provides high-quality Internet access services to users on high speed trains. In order to make the router monitorable and manageable in real-time, this paper analyzes the design requirement of the network monitoring and management system. By analyzing the principle and functional requirement of the routing system, software and hardware conditions, this paper gave an efficient and reliable network monitoring and management mechanism, and realized it with Node.js, Express, HTML5 and MySQL under Linux. The data transmission of network are monitored and managed through Web. Finally, a network topology is built to test the network monitoring and management system. The result shows that the system can satisfy the design reqiurement.

Learning Semantically Related Words in Software Through Word Embedding

HU Wang-sheng

2017, 0(9): 19-23,49. doi:10.3969/j.issn.1006-2475.2017.09.004

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Searching for previously written code is important for software development and maintenance. The same as traditional information retrieval, the inherent difficulty of keyword based code search is vocabulary mismatch between user query and retrieved code. To improve the accuracy of code search, learning semantically related words in software for query expansion is needed. This paper designs a Word Embedding based method to learn semantically related words in software, and obtains semantically related words for 19332 words through training it on Stack Overflow documents. The experiment results show that the learned semantically related words can effectively improve code search accuracy.

Recognition of Mobile Robot Natural Language Navigation Instructions Based on Word Embedding and SVM

WANG Si-yuan1, WANG Heng-sheng1,2, LIU Tong1

2017, 0(9): 24-28,55. doi: 10.3969/j.issn.1006-2475.2017.09.005

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A method based on word embedding and support vector machine (SVM) for the commands recognition of mobile robots navigation is proposed as a solution for the mobile robots navigation by natural language instructions. A language model with navigation features is trained by Skip-gram model. Feature vector is generated by word embedding and the additive combinatorial operation characteristics of Skip-gram model. Feature vector is used as the feature input of SVM to complete the task of the navigation command distinction. The method overcomes the problem that the artificial definition feature vector of SVM model is complicated and incomplete. Experiments show that this method has a good classification result and improves the average F1-measure by 2%.
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ethod of False Counterexamples Detection in Model Checking

LIU Lin-wu， ZHANG Chi

2017, 0(9): 29-32,44. doi:10.3969/j.issn.1006-2475.2017.09.006

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In model checking, abstract technology is an effective method to solve the state space explosion, but one of the major obstacles is the abstraction of original system may incur some behaviors that does not exist in the original system, i.e. may incur false counterexamples. Therefore, abstraction refinement should be done based on counterexample. How to judge a counterexample is true or false is very important in the process of abstraction refinement. According to the definition of failure state which is based on pre and post state, the definition of the false counterexample is proposed, and then two algorithms of false counterexample detection are given.

A Partitioned Elias-Fano-Golomb-Rice Index Invertal Compression Algorithm

LI Jun-ting, QU You-li

2017, 0(9): 33-39. doi:10.3969/j.issn.1006-2475.2017.09.007

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The partitioned Elias-Fano algorithm was applied to the compression of inverted indexes, showing good compression performance. This paper proves Golomb-Rice algorithm offers better compression than Elias-Fano. Combining with the basic idea ‘partition’ of partitioned Elias-Fano algorithm, we propose partitioned Elias-Fano-Golomb-Rice index compression algorithm. Experimental results show that the partitioned Elias-Fano-Golomb-Rice index compression algorithm provides better compression, compared with partitioned Elias-Fano algorithmand other compression algorithms of inverted indexes.

Design of Storage and Retrieval System for Mass Automatic Weather Station Minute Data

WANG Jian-rong, JI Gang

2017, 0(9): 40-44. doi:10.3969/j.issn.1006-2475.2017.09.008

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With the rapid growth of temporal and spatial density of ground automatic weather station, the quantity of observed data increases exponentially, traditional relational database has insufficient ability and low performance in mass data storage and retrieval. In view of this, an automatic station minute data storage and retrieval system is designed. Quartz is used to collect automatic station minute files and then the system decodes the minute files and storage decoded data into database. The system uses HBase to build minute data storage model. In order to meet the query demands of multiple elements, the system applies Elasticsearch to build secondary index of HBase. Test results show that the average storage time for minute data is 54.6 s and the secondary index with Elasticsearch is complete and reliable and the retrieval results returned time to milliseconds. Therefore, the system can satisfy the demand for storage and query performance of automatic weather station minute data.

Data Replication Model of Multi-source Database

LIU Sai, NIE Qing-jie, YUE Jun-song, LIU Jun, JIANG Jian-fei

2017, 0(9): 45-49. doi:10.3969/j.issn.1006-2475.2017.09.009

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This paper presents an interactive data replication model based on log parsing for cross-database platform, cross-operating system platform and cross-hardware platform. The model extracts the incremental change log data from the Redo log of the database, and then transfers the extracted log data to the preprocessing end using the parallel synchronous transmission method. The preprocessing end performs the transaction merging processing of the original data, and then obtains the queue data of the preprocessing end through the data subscription form on the loading publishing end, puts the data content into the local cache queue file, finally carries on the data transformation to the local cache queue file, and loads the data in storage. The comparison and analysis show that the model not only can guarantee the consistency of data, but also has a low data replication delay.
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Design and Implementation of New Secure Socket Layer Protocol Based on Identity Encryption

YE Hui-cheng, LIU Guang-jie

2017, 0(9): 50-55. doi:10.3969/j.issn.1006-2475.2017.09.010

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Secure Socket Layer protocol has been widely used in network security. The majority of current SSL protocols is based on certificate. New non-certificate cryptosystems represented by Identity-based cryptosystem can reduce the computation overhead and network load while ensuring high security, and avoiding the management of certificates. A new SSL protocol based on identity-based cryptosystem is proposed, and it is implemented using OpenSSL library and PBC library. The analysis of the communication shows that the proposed identity-based cryptosystem can establish a secure SSL connection without the certificate operation.

Design and Verification of New Defense Model for Android Mobile Phone Access Vulnerability

XIAO Cheng-wang, LU Jun, YU Li-geng

2017, 0(9): 56-60,119. doi:10.3969/j.issn.1006-2475.2017.09.011

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Today, smart phone security issues more and more arouse people’s attention. Trojan, as a highly hidden and deceptive attack means, has continuously spreading on the platform, although has been widespread concern, but there is no good way to suppress. The right to mention the loopholes for the Android system security threats is enormous. Once the attacker has the opportunity to leak through the kernel memory address of the kernel, he can close the kernel memory write protection to write the kernel memory to execute malicious commands permissions, and ultimately achieve the purpose of downloading Trojans. In order to deal with this vulnerability, we first analyze SEAndroid mechanism, and propose a kernel vulnerability prevention module combining kernel enhancement and packet filtering. Based on this mechanism, we verify the validity of the proposed prevention module.

Pavement Crack Extraction Based on Hessian Matrix and Linear Structure

ZHU Chun-xing, CAI Qian, XU Gui-li, LI Zhen-hua, WANG Zheng-bing

2017, 0(9): 61-66. doi:10.3969/j.issn.1006-2475.2017.09.012

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At present, the result of the 3D pavement crack detection method is bad. This paper proposes a new pavement crack extraction method based on Hessian matrix and linear structure. Firstly, in order to solve the problem of weak contrast and multi interference of pavement depth image, according to the characteristics of edge and gray level of crack, a new algorithm based on piecewise function is proposed. Then, in view of the issue of discontinuous crack and sensitivity to noise of multi scale rough extraction algorithm based on Hessian matrix, according to the consistency of the local direction of crack and the discreteness of noise, a new algorithm based on linear structure constraint is proposed. The experimental results show that the method is suitable for pavement cracks with different scales and degrees, compared with the method of document［5］ and document［15］, the extraction integrity is improved by more than 10% and more than 6% respectively, the extraction correct rate is increased by more than 2% and more than 1% respectively, but the time is almost the same as the first and is about one third of the second.

A Roadside Parking Detection and Recognition Method Based on Video Motion Trail

YU Qing-qing1, ZHANG Ke2, TANG Heng-liang3, ZHAO Jing2, GE Yun1, WANG Ming4

2017, 0(9): 67-73. doi:10.3969/j.issn.1006-2475.2017.09.013

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An effective detection and recognition method is proposed for the roadside parking problem by combining the KLT corners detection approach and real-time compressive tracking (CT) approach. First, KLT approach is used to detect corner in video for extracting the area of moving car. And then the CT approach is adopted to acquire the motion trail for each parking action to describe the dynamic process of moving cars completely. The proposed method is tested on the real video data for evaluation. The results demonstrate that this method is feasible and effective.

esearch and Application on Association Rule Mining Optimization Algorithm for High Speed EMU Malfunction

ZHANG Chun, ZHOU Jing

2017, 0(9): 74-78. doi:10.3969/j.issn.1006-2475.2017.09.014

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As high-speed and safe railway transportation, EMU is hard to avoid malfunction. In the view of the characteristics of EMU malfunction data—huge volume and low value density, we design an improved association rules mining algorithm based on the DHP algorithm. We adopt rehashing technique to solve the hash collision in the DHP algorithm and propose the RDHP algorithm, which can filter out all the infrequent item sets without additional database scanning. In order to enhance the efficiency of the algorithm, we propose the MR-RDHP algorithm based on the MapReduce programming framework. It decomposes the task of EMU malfunction data association rule mining to massive parallel processing on multiple computers in the cluster. The experimental result shows that the MR-RDHP algorithm has a good time performance and the rules dug out can be effectively used to guide EMU operation and maintenance.

Improved Multiobjective Evolutionary Algorithm for Solving Flexible Job Shop Scheduling Problem

ZHANG Chun-yan

2017, 0(9): 79-84. doi:10.3969/j.issn.1006-2475.2017.09.015

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Aiming at the processing time for fuzzy flexible job shop scheduling problem and considering minimizing the fuzzy maximal makespan, fuzzy total workload and fuzzy critical workload as optimization objectives, an effective multiobjective evolutionary algorithm is proposed. A method of mixing different machine allocations and operation sequencing strategies is adopted to generate initial population and a well-designed greedy inserting algorithm is adopted for chromosome decoding. A Pareto dominant relation based on possibility degree and a modified crowding distance measure in decision space are defined and further employed to improve the fast nondominated sorting. Moreover, a novel local search based on fuzzy critical path theory is first incorporated into MOEA. Afterwards, the influence of key parameters is investigated by designing of the Taguchi method of design of experiment. Finally, extensive comparison with three existing algorithms is carried out, and the results demonstrate the effectiveness of the proposed MOEA in solving multiobjective flexible job shop scheduling problem (MOfFJSP).

An Algorithm of Coverage-Hole Patching on Improved Rhomboid Mesh

ZHU Lin

2017, 0(9): 85-88,126. doi:10.3969/j.issn.1006-2475.2017.09.016

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The coverage holes seriously affect the performance of the target monitoring network. This paper proposes an algorithm of coverage-hole patching on the improved rhomboid mesh in hybrid sensor networks. This algorithm guides the mobile nodes to the specified location to eliminate the holes region through the improved rhomboid shaped patch. The simulation results show that, compared with TNR algorithm, the proposed algorithm can effectively reduce the number of mobile nodes, and improve the utilization ratio of mobile nodes.

Multi-target Prediction Algorithm Based on AdaBoost Regression Tree

ZHANG Jing1,2

2017, 0(9): 89-95,105. doi:10.3969/j.issn.1006-2475.2017.09.017

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Real word prediction problems typically involve the simultaneous prediction of multiple target variables using the same set of predictive variables. When the target variables are binary, the prediction task is called multi-label classification, and when the target variable is real-valued, it is called multi-target prediction. This paper puts forward two new multi-target regression algorithms: Multi-Target Stacking(MTS) and Ensemble of Regressor Chains(ERC) which are inspired by two popular multi-label classification methods. Both MTS and ERC have the same baseline method, which are based on the single-target (ST) prediction model that is established by using 100 regression tree AdaBoost iterative algorithms. However, MTS and ERC extend the input space of the second stage by adding the target prediction value of the first stage. Both methods take into account the dependencies between the target variables, besides, ERC takes into account the order selection between targets. In addition, we also summarize the shortcomings of MTS and ERC methods, and propose the corrected versions denoted as MTS Corrected (MTSC) and ERC Corrected (ERCC). Experimental results show that the modified regression chain ERCC performs best in multi-objective prediction problems.

Cable Fault Diagnosis Based on HHT Transform and FOA_LSSVM

SU Li

2017, 0(9): 96-101,105. doi:10.3969/j.issn.1006-2475.2017.09.018

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In view of the shortcomings of the existing methods of fault diagnosis for underground cables with low accuracy and large error, a method of cable fault diagnosis based on HHT transform and FOA_LSSVM is proposed. According to the underground cable fault signal, through the HHT transform to extract the feature component of underground cable fault signals, the characteristic components and underground cable fault types were extracted as the input and output of FOA_LSSVM, to identify the fault types of underground cables. Taking 150 sets of underground cable fault data as the test object, the experimental results show that FOA_LSSVM has higher accuracy than GA_LSSVM, PSO_LSSVM and DE_LSSVM, and is more suitable for the diagnosis and identification of underground cable fault.

Speeding K-NN Classification Method Based on Testing Sample Label

WANG Xiao, ZHAO Li

2017, 0(9): 102-105. doi: 10.3969/j.issn.1006-2475.2017.09.019

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To solve the problem of the low prediction efficiency of traditional K-NN classification, this paper presents a speeding K-Nearest Neighbor (K-NN) classification method based on testing sample label (KNN_TSL). Firstly, a certain number of testing samples is obtained by traditional K-NN classification method. Then for the samples to be entered latterly, the distance between the labeled samples and the testing sample is calculated. If the distance is less than a given threshold, the new entry sample is assigned the same class label. Otherwise, the K-NN classification is performed. By this method, most last easily classified samples can be decided only by considering the relationship of it with the labeled testing samples, and only a small number of samples is reclassified. Because the labeled samples contain some information of class, this method can greatly improve the classification prediction efficiency and ensure the generalization performance. The experiment result demonstrates that the proposed KNN_TSL model can obtain the high learning efficiency and testing accuracy simultaneously.

Factor Analysis and Empirical Study on Effect of Learner’s Learning in Online Teaching

CHEN Guo-xin， HAO Xuan-wen， QIU Guo-yong， WU Zhen-qiang

2017, 0(9): 106-110. doi:10.3969/j.issn.1006-2475.2017.09.020

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In the educational environment of informatization and globalization, online education platforms such as MOOC, Khan Academy, and Higher Quality Courses emerge. Improving the efficiency of student learning has become one of the challenges facing online education.At present, the researchers have different attitudes towards the factors that influence the learning effect in the online learning process. This paper explores and validates the factors that influence learners’ learning efficiency in the process of online teaching by using the online education platform data of a university. After analyzing the characteristics of statistical and association rules mining algorithm, the data are preprocessed, analyzed by statistical and Apriori correlation analysis algorithm, and the results are presented by visualization.The results show that the average scores of teachers’ marking work are negatively correlated with the workload of teachers’ marking. The students’ online work is generally “delayed”; the learning effect is positively correlated with the number of login, online time and online discussion. Finally, the results of the analysis are given to improve the learning effect of the online learning process.

Design of Intelligent Guide System Based on Android Mobile Phone

GUO Qi-qing, LI Wei

2017, 0(9): 111-113,119. doi: 10.3969/j.issn.1006-2475.2017.09.021

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With the development of information technology, intelligent tourism gradually spreads. Aiming at the existing problems of tourism information asymmetry, and in view of user experience perspective, this paper designs an Android-based intelligent guide system. We describe the design architecture of this system, and introduces every component modules such as the map location, path planning, multimedia share and so on. Test result shows that the system realizes the intelligent interactive of tourism, provides a positive user experience to the self-help travelers.

OpenStack Storage Clone Chain Based on Ceph

PENG Xiao1,2, ZHANG Jun2, YIN Zhao2

2017, 0(9): 114-119. doi:10.3969/j.issn.1006-2475.2017.09.022

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When OpenStack uses Ceph as the storage backend, the OpenStack cloud platform storage management is prone to error and inefficiency due to Ceph’s internal cloning mechanism. In order to solve and optimize the storage management technology deficiencies when OpenStack uses Ceph as a storage backend, through Ceph queries each object’s backend form based on Openstack, this paper recursively finds children-links of volume or snapshot, implements OpenStack storage Clone Chain function, and clone chain is visualized by Django and Angular JS. Through the display of the clone chain, we can clearly understand the relationship between the various storage objects OpenStack, so that to improve the efficiency of OpenStack storage management.

Design of Mine Environment Monitoring and Personnel Positioning System Based on ZigBee

XIAO Ling-lu1,2

2017, 0(9): 120-126. doi:10.3969/j.issn.1006-2475.2017.09.023

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In view of the complicated environment of coal mine, the frequent occurrence of gas explosion accidents, and the difficulty of mine accident rescue, etc, a mine environment monitoring and personnel positioning system based on ZigBee is designed. The environmental parameters such as temperature, humidity and gas concentration are collected by the sensor, the CC2530 is used to build data transmission and personnel location network, the RSSI positioning technology and the three side positioning algorithm are combined to realize the real-time positioning of underground workers. According to the characteristics of the underground environment, the mean value filtering algorithm is used to modify the RSSI value, and the positioning accuracy is improved. The experimental results show that the system has the advantages of accurate monitoring of environmental parameters, smooth and reliable network communication and high positioning accuracy, and it can meet the needs of coal mine safety production management and mine accident rescue.

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