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ISSN 1000-0585
CN 11-1848/P
Started in 1982
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  • Table of Content
      , Volume 58 Issue 8 Previous Issue    Next Issue
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    COMPUTER SCIENCE AND TECHNOLOGY
    Pattern router to regulate dynamic actions in the router dataplane
    XU Lei, XU Ke
    Journal of Tsinghua University(Science and Technology). 2018, 58 (8): 693-697.   DOI: 10.16511/j.cnki.qhdxxb.2018.21.019
    Abstract   PDF (1606KB) ( 360 )
    Router security has become more important with the increasing number of programmable routers. This paper presents a pattern router that codes the modularized dataplane and pre-combines the result to monitor and regulate the dynamic actions in the dataplane. This method uses an action identifier (AID) for each action in the dataplane and puts the normal AID into a regulated action table (RAT) before running the router. When the router is working, all the dynamic actions are verified by the RAT to secure the honesty of each action. The pattern router was implemented in a Click router and in a data plane development kit (DPDK) router with tests showing that the pattern router occupies only 2 MB and uses less than 10% of the bandwidth to capture all the abnormal actions in the dataplane.
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    Variational autoencoder with side information in recommendation systems
    LIU Weidong, LIU Yaning
    Journal of Tsinghua University(Science and Technology). 2018, 58 (8): 698-702.   DOI: 10.16511/j.cnki.qhdxxb.2018.21.016
    Abstract   PDF (915KB) ( 368 )
    The variational autoencoder (VAE) unsupervised learning method can provide excellent results in recommendation systems. Recommendation systems seek to accurately identify a missing value with the VAE learning a latent factor from the input and then predicting when to use this for reconstructing the result. Side information was added to the VAE to improve the predictions with tests on datasets including MovieLens and grades data showing that it can significantly improve the prediction accuracy by up to 31% with enough side information with the grades dataset.
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    Effect of snippet text bolding in search user behavior
    ZHANG Hui, SU Ning, LIU Yiqun, MA Shaoping
    Journal of Tsinghua University(Science and Technology). 2018, 58 (8): 703-709.   DOI: 10.16511/j.cnki.qhdxxb.2018.25.033
    Abstract   PDF (5709KB) ( 361 )
    Search users rely on result captions including titles, snippets and URLs to decide whether they should click a particular result. Snippets usually serve as a query-dependent summary of its corresponding landing page and are, therefore, one of the most important factors in the search interaction process. At present, commercial search engines use query bolding strategies, but these have various problems and lack useful information. This paper presents a bolding strategy that improves user search efficiency. The method includes three bolding strategies based on crowd sourcing results which differ from the query terms strategy. Tests show that the search behavior is affected by the term bolding strategies without changes in the snippet contents. The tests also show that the responses to the three bolding strategies are better than responses to the query terms bolding strategy to produce a better bolding strategy. The appropriate bolding numbers, bolding ratio, and targeted information have a very positive impact on the user's search behavior.
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    Single node failure routing protection algorithm based on segment routing
    GENG Haijun, LIU Jieqi, YIN Xia
    Journal of Tsinghua University(Science and Technology). 2018, 58 (8): 710-714.   DOI: 10.16511/j.cnki.qhdxxb.2018.22.040
    Abstract   PDF (1785KB) ( 289 )
    Existing routing protection schemes do not accurately consider the relationships between the failure protection ratio and the path stretch. A simple IP fast reroute based on the segment routing (IPFRRBSR) algorithm is given here to consider these relationships. IPFRRBSR calculates two paths between each source-destination pair with one being the shortest path and the other being a backup path constructed using segment labels. The packets are forwarded along the shortest path when the network is in the normal state, but are forwarded along the backup path when a network failure occurs. Since the shortest path and the backup path (except for the source and destination nodes) do not have any common nodes, the probability of them failing simultaneously is very low. Tests show that IPFRRBSR can deal with single node failures in the network and has a small path stretch.
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    Evaluating online services based on a ranked pairs social choice function
    FU Xiaodong, LI Jun, LIU Li, YUE Kun, FENG Yong, LIU Lijun
    Journal of Tsinghua University(Science and Technology). 2018, 58 (8): 715-724.   DOI: 10.16511/j.cnki.qhdxxb.2018.21.018
    Abstract   PDF (1312KB) ( 312 )
    Different customers may have different evaluation criteria which leads to different ratings of online services. Thus, aggregation of the ratings cannot objectively evaluate the services. This paper presents an online services evaluation method based on a ranked pairs social choice function. The method uses preference relations rather than ratings to evaluate the online services. First, the preference relations of the customer are calculated based on a ratings matrix. Then, a list of online service pairs is established according to the service priority relationship determined from the majority rule. Finally, a directed acyclic graph is constructed and a path is found in the graph that contains all the online services. The service order in the path is then used to evaluate the services. A theoretical analysis and tests verify the reasonability and effectiveness of this method.
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    Road segmentation using full convolutional neural networks with conditional random fields
    SONG Qingsong, ZHANG Chao, CHEN Yu, WANG Xingli, YANG Xiaojun
    Journal of Tsinghua University(Science and Technology). 2018, 58 (8): 725-731.   DOI: 10.16511/j.cnki.qhdxxb.2018.21.013
    Abstract   PDF (2135KB) ( 385 )
    Common road segmentation methods are often limited by environmental noise and the roughness of the segmenting edges. A road segmentation method was developed to address these shortcomings by combining a fully convolutional neural network and a conditional random field. The feature representation in the neural networks models the road segmentation as a binary classification problem. A VGG-16 deep convolutional neural network based fully convolutional network was constructed to classify each road image end to end into the road and the background. Then, the fully-connected conditional random field (CRF) was used for fine segmentation to refine the coarse edges obtained from the binary classification. Tests of road segmentation benchmark datasets acquired in real environments show that this method can achieve 98.13% segmentation accuracy and real-time processing with 0.84 s perimage.
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    Neighborhood density grid clustering and its applications
    SUO Mingliang, ZHOU Ding, AN Ruoming, LI Shunli
    Journal of Tsinghua University(Science and Technology). 2018, 58 (8): 732-739.   DOI: 10.16511/j.cnki.qhdxxb.2018.22.025
    Abstract   PDF (4356KB) ( 372 )
    The clustering data analysis tool plays a significant role in various fields such as pattern recognition, bibliometrics and fault diagnosis. This paper describes a clustering approach based on neighborhood relationships, local densities and spatial grid partitions. The time complexity of this algorithm is reduced using a spatial grid with the clustering elements searched using neighborhood density relationships in the grid space. Cluster centers are then selected automatically using the maximum relative distance and the maximum relative local density. Tests on artificial data indicate that neighborhood density grid clustering can automatically cluster data and effectively process data with arbitrary shapes. Comparisons using regional recognition datasets demonstrate that this method is more suitable for clustering complex data with unusual shapes.
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    MECHANICAL ENGINEERING
    Experimental study of the T(0,1)-wave excitation method based on a buffer waveguide
    SUN Feiran, DING Yulin, SUN Zhenguo, CHEN Qiang, MURAYAMA Riichi
    Journal of Tsinghua University(Science and Technology). 2018, 58 (8): 740-745.   DOI: 10.16511/j.cnki.qhdxxb.2018.21.014
    Abstract   PDF (5382KB) ( 184 )
    This paper presents an experimental study of the T(0,1) mode guided wave excitation method for a buffer waveguide wrapped around a pipe which has been proposed to overcome the main challenge in online inspection of high-temperature pipelines. The dispersion curve was analyzed in tests to show that the T(0,1) mode guided wave can be excited in the pipe by exciting the S0 mode Lamb wave in the buffer waveguide with a meander-line coil EMAT to generate the S0-wave and a PPM EMAT to detect the T(0,1)-wave. Tests indicate that the T(0,1)-wave can be converted from the S0-wave by the buffer waveguide, which verifies the validity of this method as a foundation for engineering applications.
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    Accuracy of an electric spindle
    WANG Liping, ZHAO Qinzhi, ZHANG Binbin
    Journal of Tsinghua University(Science and Technology). 2018, 58 (8): 746-751.   DOI: 10.16511/j.cnki.qhdxxb.2018.26.035
    Abstract   PDF (2747KB) ( 375 )
    An electric spindle, as the core of a CNC machine center, directly affects the accuracy of the CNC machining center. This paper describes a method to evaluate electric spindle accuracy by analyzing the spindle radial, tilt and axial motion errors. A radial error model of the electric spindle is then developed from the least squares circle. A rapid evaluation method of the radial accuracy uses a least squares circle approximation algorithm. The radial error is then used to calculate the tilt error and the tilt angle of the spindle rotation axis. Time and frequency domain signal analyses are used to extract the eigenvalues of the axial error to evaluate the axial positioning accuracy of the spindle. This comprehensive accuracy analysis method was applied to a high-speed electric spindle with the results showing that with as the rotational speed increased, the radial error increased while the tilt error and the axial error remained constant. This accuracy analysis method can be applied to analyzed the performance and accuracy degradation of spindles.
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    HYDRAULIC ENGINEERING
    Effect of turbulence models on the simulation of the flow in a complex asymmetric penstock
    CHEN Wenchuang, ZHANG Rui, ZHANG Wenyuan, ZHANG Jinxiong, ZHANG Dong
    Journal of Tsinghua University(Science and Technology). 2018, 58 (8): 752-760.   DOI: 10.16511/j.cnki.qhdxxb.2018.26.037
    Abstract   PDF (5377KB) ( 232 )
    This study analyzes the effects of various turbulence models on the hydrodynamic simulation accuracy and computing time for flow in a complex asymmetric penstock. The turbulence models used here for closure of the Reynolds-averaged Navier-Stokes (RANS) equations were the shear-stress-transport k-ω (SST k-ω), standard k-ε (Sk-ε), realizable k-ε (Rk-ε), renormalization group k-ε (RNG k-ε) and Reynolds stress model (RSM) models. The results show the differences in the velocity, turbulent kinetic energy, and water head loss predictions and the computing times for these five turbulence models. The predictions are compared with experimental data to show that the computing times and the differences between the numerical and experimental results vary with the flow conditions. The RSM results agree best with the experimental results, while the SST k-ω model costs less CPU time. Both the RSM and SST k-ω are found to be appropriate for calculating the hydraulic characteristics of the flow in the complex asymmetric penstock, depending on the available computing resources and the required accuracy. The Sk-ε, Rk-ε and RNG k-ε models all give lower accuracy predictions of the flow distribution, confluence and water head loss.
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    ENVIRONMENTAL SCIENCE AND ENGINEERING
    Factors impacting the regional air quality in the Guangdong-Hong Kong-Macao Greater Bay Area: A study based on grey relational analysis
    ZHAN Shexia, KUANG Yaoqiu, RUAN Zhu
    Journal of Tsinghua University(Science and Technology). 2018, 58 (8): 761-767.   DOI: 10.16511/j.cnki.qhdxxb.2018.26.031
    Abstract   PDF (2230KB) ( 300 )
    This study investigates the temporal and spatial variations of air pollutants in the Guangdong-Hong Kong-Macao Greater Bay Area during 2006-2016 based on the Guangdong-Hong Kong-Macao Pearl River Delta Regional Air Quality Monitoring Network data to identify the key factors affecting the pollutants. The correlation between social-economic factors and the pollutant concentrations is also analyzed using a grey relational analysis. The air quality monitoring results showed that the average annual concentrations of SO2, NO2 and PM10 generally decreased over the study period while the average monthly concentrations had U-shaped curves. However, the average annual O3 concentration increased dramatically with its monthly variations having M-shape curves during each year. The results indicate that industry, energy consumption, population and environmental management are the main factors affecting the air quality in the Greater Bay Area. Thus, the main pathways for improving the air quality in the Greater Bay Area are tough industrial pollution regulations, reduced energy consumption and strengthened vehicle emission control measures.
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    PHYSICS AND ENGINEERING PHYSICS
    Analysis method for standardization reviews on Beijing enterprises
    ZHU Yu, CHEN Tao, JI Xuewei, ZHANG Hui, WU Aizhi
    Journal of Tsinghua University(Science and Technology). 2018, 58 (8): 768-772.   DOI: 10.16511/j.cnki.qhdxxb.2018.25.034
    Abstract   PDF (2607KB) ( 230 )
    There are over one million items from ten thousand enterprises in the Beijing Enterprise Standardization Review. Big data analytical methods can be used to analyze the relationships between the deduction counts of the review items because of the large data volume. The most popular method is association rules, but these are qualitative, not quantitative. Neural network, another widely used data mining method, are able to solve complex non-linear problems but requires much effort to choose the suitable inputs and targets. This article combines these two methods with the association rules used to select the inputs and targets from the review items and the neural network used to relate the inputs and the targets. A test gave a strong correlation between 3 selected review items and 8 other review items with a correlation coefficient of the fitting curve of over 0.84 between the predicted targets and the real value. Thus, this combined method can improve data mining of the enterprises review items with the result used to predict the deduction counts of the selected items.
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