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ISSN 1000-0585
CN 11-1848/P
Started in 1982
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  • Table of Content
      , Volume 60 Issue 4 Previous Issue    Next Issue
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    MECHANICAL ENGINEERING
    Automatic recognition and control of welding arc morphology as a function of the welding current and the tungsten electrode height
    ZHU Zhiming, CHENG Shijia, YU Yingfei, FU Pingpo
    Journal of Tsinghua University(Science and Technology). 2020, 60 (4): 285-291.   DOI: 10.16511/j.cnki.qhdxxb.2019.26.037
    Abstract   PDF (5421KB) ( 286 )
    A welding arc has very different geometric morphologies depending on the welding process parameters that can change during the weld. High speed photography and image processing were used to develop a discriminating model to evaluate the arc morphology while welding using a polar coordinate system. The model was then used to study the quantitative relationship between the arc morphology and the tungsten electrode height for various welding currents. The experimental results show that various welding currents and tungsten electrode heights lead to four typical arc morphologies with oblate conical shapes, bell jar shapes, long conical shapes and long strip shapes. The range of tungsten electrode heights corresponding to each arc morphology were found to vary for different welding currents. With the bell jar shaped arc, the arc has smaller lateral oscillations, which gives a more stable welding arc and better welding seam quality. A control strategy was then developed based on the arc morphology to control the tungsten electrode height for real-time control of the tungsten electrode height during automatic welding.
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    Geometric error measurement and accuracy verification of the swing head of a five-axis CNC machine tool with a single swing angle
    WANG Liping, ZHANG Shizhen, WANG Dong
    Journal of Tsinghua University(Science and Technology). 2020, 60 (4): 292-298.   DOI: 10.16511/j.cnki.qhdxxb.2020.25.006
    Abstract   PDF (3557KB) ( 501 )
    The swing angle accuracy is a key factor which strongly affects the machining performance of five-axis CNC machine tools and accurate measurements of the geometric error. This study measured the geometric swing head error of a five-axis CNC machine tool designed for machining aero-engine casings to verify the accuracy. This paper described a machine tool structure with a single direct-drive swing head and measurements of the main geometric errors of the swing head, including the deviation from the spindle end-face to the rotational center of the A-axis, the deviation between the swing sector and the YZ plane, and the height difference between the spindle axis and the A-axis. Then, the swing angle positioning accuracy was measured with the related position error. A cutting test on a simulated aero-engine casing showed that the machine tool swing angle accuracy was sufficiently accurate to properly machine the casing. The methods presented in this paper provide a foundation for improving the machining accuracy of five-axis machine tools in industry.
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    Performance degradation data based NC rotary table reliability predictions using a single sample
    ZHANG Yun, YU Guang, WANG Liping, JIANG Nan
    Journal of Tsinghua University(Science and Technology). 2020, 60 (4): 299-305.   DOI: 10.16511/j.cnki.qhdxxb.2019.22.049
    Abstract   PDF (1783KB) ( 345 )
    Reliability evaluations of numerical control (NC) rotary tables are limited by their good reliability, long lives and few samples. This paper presents a reliability evaluation method based on performance degradation data for NC rotary tables. The test data is expanded by measuring the performance degradation of a rotary table at different angles to calculate a pseudo-life for the rotary table. The accuracy of the rotary table reliability evaluation is improved using the virtual augmentation principle to expand the test data with the virtual augmented samples analyzed mathematically. The results show that this method can be used to determine various reliability indexes such as the reliability, failure distribution density function and predicted average life of NC rotary tables. This method significantly reduces the test costs and time.
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    ELECTRONIC ENGINEERING
    Radio resource management in integrated terrestrial HAP MBMS systems
    DUAN Haining, ZHANG Yu, SONG Jian
    Journal of Tsinghua University(Science and Technology). 2020, 60 (4): 306-311.   DOI: 10.16511/j.cnki.qhdxxb.2019.21.032
    Abstract   PDF (1838KB) ( 470 )
    Highaltitude platform (HAP) communication systems provide wide coverage, low networking costs, and large capacities for reliable multimedia broadcast/multicast services (MBMS) for terrestrial users. This paper presents an algorithm that jointly optimizes the downlink multiple access and the resource allocation for cooperative broadcasting using an integral HAP/terrestrial system which takes into account both the energy and spectrum efficiencies. The location distribution of active users is used to select the appropriate channel resources for each service that minimizes the energy use. Then, a hybrid orthogonal and non-orthogonal multiple access system is designed to satisfy the quality of service requirements for each service in the same channel to balance the spectral efficiency and the receiver complexity. Simulations verify that this algorithm is more efficient than the traditional orthogonal multiple access (OMA) strategy and single channel transmission algorithms. This algorithm has less complexity loss, significantly reduces transmission costs and provides more flexible radio resource management.
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    Parallel algorithm for solving sparse linear equations based on variable correlation decomposition
    LIU Shanxia, JIN Song, WANG Wenchen, WANG Yuhang, WANG Yu
    Journal of Tsinghua University(Science and Technology). 2020, 60 (4): 312-320.   DOI: 10.16511/j.cnki.qhdxxb.2020.25.005
    Abstract   PDF (2868KB) ( 413 )
    Sparse linear equations are the core of many large scientific computing tasks. Although parallel algorithms are providing powerful, new tools for solving sparse linear equations, existing parallel algorithms have some drawbacks such as the optimal sub-matrix being difficult to obtain and the algorithms requiring a large overhead to synchronize parallel tasks. This paper presents a parallel algorithm for sparse linear equations based on the variable correlation decomposition method. Firstly, the algorithm performs an incomplete LU decomposition on the coefficient matrix to obtain two equations for the upper and lower triangles. Then, the correlation between y and x is used to solve for the independent part of the variable in parallel from the upper and lower triangles. All the individual partial values are then added to get the final value of the variable. Since the solution does not need to wait for all the previous variables to be calculated, the partial value calculation can be performed in parallel as the calculation proceeds, which significantly reduces the algorithm execution time and improves the algorithm solution speed and parallelism. Tests show that this sparse linear equation solver is more than 50% faster than the parallel solution method in the cusparse library.
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    PHYSICS AND ENGINEERING MECHANICS
    Risk assessment model for building fires based on a Bayesian network
    SHU Xueming, YAN Jun, HU Jun, WU Jinjin, DENG Boyu
    Journal of Tsinghua University(Science and Technology). 2020, 60 (4): 321-327.   DOI: 10.16511/j.cnki.qhdxxb.2019.26.036
    Abstract   PDF (1884KB) ( 819 )
    The development of building fires was divided into four stages for risk assessment as fire initiation, fire alarm, fire behavior, and fire spreading based on fire engineering theory with analyses of the main risk assessment parameters of each stage. The dynamic risk assessment model was based on a Bayesian network. A sensitivity analysis was then used to evaluate the influences of key parameters on the fire risk. Two typical buildings were then used as examples to evaluate the risk at each fire stage and the overall risk. The results illustrate how the building fire risk is a dynamic process with different risk and impact parameters in each stage. The model nodes and dependencies constitute a causal network. The evaluation model can effectively combine large amounts of fire data collected by a building fire monitoring terminal using artificial intelligence analyses. This research can effectively improve building fire safety management.
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    Social network analysis of typical women trafficking networks
    HUANG Zhongliang, WENG Wenguo
    Journal of Tsinghua University(Science and Technology). 2020, 60 (4): 328-333.   DOI: 10.16511/j.cnki.qhdxxb.2019.26.045
    Abstract   PDF (2034KB) ( 521 )
    Women trafficking is a very serious problem in China which disrupts social order and endangers public security. Women trafficking cases were divided into three categories according to the characteristics of the victims with one typical case selected from each category to construct the trafficking networks. Then, the entire network characteristics including the actors' characteristics and the factors influencing co-offending were studied. The results show that the trafficking networks present a star-shaped structure with clear core-periphery structures. Co-offending is more likely to take place within the same nationality or native place. In addition, there is a significant positive effect of kinship on the co-offending. The findings in this paper provide a reference to help police prevent, control and combat women trafficking.
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    Kalman filtering and bio-heat transfer model based real-time MR temperature imaging for increased accuracy
    WU Jinchao, QIU Shihan, LI Muheng, WEI Xing, CHEN Bingyao, YING Kui
    Journal of Tsinghua University(Science and Technology). 2020, 60 (4): 334-340.   DOI: 10.16511/j.cnki.qhdxxb.2019.22.039
    Abstract   PDF (8164KB) ( 215 )
    Magnetic resonance temperature imaging is an important technique to ensure safe and effective use of tumor hyperthermia ablation by providing real-time, global temperature field monitoring. In clinical trials, however, the signal-to-noise ratio (SNR) of magnetic resonance temperature imaging is relatively low, with the signal quality degrading more with fast imaging sequences. To solve this problem, a bio-heat transfer based Kalman filtering model is developed for magnetic resonance temperature imaging where the bio-heat transfer equation is transformed into the form of a Kalman state transition matrix. Then the simulated temperature is combined with the measured temperature to create an accurate, high SNR estimated temperature. Clinical simulations show that this method reduces the temperature measurement root mean square error from 6℃ to 2℃ and the physical phantom experiment shows that this method reduces the root mean square error of the measured temperature and the true temperature from 1.927℃ to 0.735℃ while significantly improving the SNR.
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    Degassing membrane for 14C detection in water
    HE Jingtao, LIANG Manchun, CHEN Anying, YUE Feng, HE Shuijun
    Journal of Tsinghua University(Science and Technology). 2020, 60 (4): 341-347.   DOI: 10.16511/j.cnki.qhdxxb.2019.26.047
    Abstract   PDF (2299KB) ( 279 )
    Current methods for detecting 14C in water are very slow and use large amounts of nitrogen. This study used a degassing membrane to improve the 14C detection in water. A 14C water separation device built using the degassing membrane was evaluated experimentally for acidic conditions to evaluate the effects of various factors on the nitrogen consumption and time. The experimental results show that the 14C separation in water using the degassing membrane has a 95% inorganic carbon recovery rate with short operating times and less nitrogen consumption.
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    CIVIL ENGINEERING
    Quantitative decision-making model for building facility maintenance strategies based on the RCM method
    MA Zhiliang, XIANG Xinglei, REN Yuan
    Journal of Tsinghua University(Science and Technology). 2020, 60 (4): 348-356.   DOI: 10.16511/j.cnki.qhdxxb.2019.22.045
    Abstract   PDF (1304KB) ( 328 )
    The lack of scientific decision-making methods for building maintenance leads to long-term high maintenance costs. This study introduces the reliability centered maintenance (RCM) method to improve building maintenance decision-making. The building facility system is first analyzed to identify key facilities. Then, the failure risk of the key building facilities is evaluated in terms of the failure rate, failure detectability and failure consequences. A quantitative maintenance decision-making model is then developed for the key building facilities and is solved using Monte Carlo simulations. Finally, a building air-conditioning system air duct is used as an example to verify the model effectiveness. The results show that this model reduces maintenance costs by about 18.5% compared with the current maintenance strategy.
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    Standardized data extraction techniques for building utility consumption and environmental monitoring systems
    HU Zhenzhong, YUAN Shuang
    Journal of Tsinghua University(Science and Technology). 2020, 60 (4): 357-364.   DOI: 10.16511/j.cnki.qhdxxb.2019.21.037
    Abstract   PDF (1932KB) ( 258 )
    China's domestic building energy consumption and environmental monitoring industry is suffering from the absence of a universal standard. As a result, different monitoring systems tend to be mutually incompatible and the vast majority of the energy consumption and environmental monitoring data cannot be integrated into a comprehensive database which leads to serious waste of data resources. This research defines a building energy consumption and environmental parameters data model and then presents a method for the extraction and integration of dynamic building monitoring data. Software modules have been implemented and tested on practical projects. This papers concludes with discussions of the problems and prospects of these techniques.
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