Showing 2,081 - 2,100 results of 3,965 for search 'GCOB~', query time: 1.43s Refine Results
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    An Experimental Investigation on the Relationship between MS Frequency Response and Coal and Gas Outburst by Quanjie Zhu, Qingsong Li, Yu Feng, Xianwei Heng, Shaoquan Li, Tao Yang

    Published 2018-01-01
    “…Microseismic (MS) frequency response is an important part of high-efficiency data mining to achieve the aim of coal and gas outburst (CGOB) early warning. Based on the variation pattern of acoustic emission (AE) signal in the coal failure process, the experimental characteristics of MS activity and typical signals CGOB were obtained in this study. …”
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  4. 2084

    Growth, Yield, and Sugar Content of Different Varieties of Sweet Corn and Harvest Time by St. Subaedah, Edy Edy, Kiky Mariana

    Published 2021-01-01
    “…Variables measured consisted of plant height, cob length, cob weight, estimation of cob weight per hectare, and sugar content. …”
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    Multiscale study on the compressive performance of diverse solid waste backfill bodies by Hang Yin, Jiepeng Liu, Xuhong Zhou, Hongtuo Qi, Shuxian Liu, Shuai Pang

    Published 2025-01-01
    “…Geomechanics and Geophysics for Geo-Energy and Geo-Resources…”
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    Experimental study of the impact of deck-charge structure on blast-induced fragmentation by Zhixian Hong, Ming Tao, Shurong Feng, Hao Liu, Wenhong Wu, Xudong Li, Shuai Liu

    Published 2025-01-01
    “…Geomechanics and Geophysics for Geo-Energy and Geo-Resources…”
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  20. 2100

    Graph compression algorithm based on a two-level index structure by Gaochao LI, Ben LI, Yuhai LU, Mengya LIU, Yanbing LIU

    Published 2018-06-01
    “…The demand for the analysis and application of graph data in various fields is increasing day by day.The management of large-scale graph data with complicated structure and high degree of coupling faces two challenges:one is querying speed too slow,the other is space consumption too large.Facing the problems of long query time and large space occupation in graph data management,a two-level index compression algorithm named GComIdx for graph data was proposed.GComIdx algorithm used the ordered Key-Value structure to store the associated nodes and edges as closely as possible,and constructed two-level index and hash node index for efficient attribute query and neighbor query.Furthermore,GComIdx algorithm used a graph data compressed technology to compress the graph data before it directly stored in hard disk,which could effectively reduce the storing space consumption.The experimental results show that GComIdx algorithm can effectively reduce the initialization time of the graph data calculation and the disk space occupancy of the graph data storing,meanwhile,the query time is less than common graph databases and other Key-Value storage solutions.…”
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