New Support Vector Machines Study Findings Reported from Beijing University (Res earch On High-frequency Torsional Oscillation Identification Using Tswoa-svm Bas ed On Downhole Parameters)
New Support Vector Machines Study Findings Reported from Beijing University (Res earch On High-frequency Torsional Oscillation Identification Using Tswoa-svm Bas ed On Downhole Parameters)
北京大学支持向量机研究新成果(基于井下参数的tswoa-svm高频扭振识别研究)
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摘要
由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑新闻-研究人员详细介绍了支持向量Mac Hines中的新数据。根据来自…的消息中华人民共和国北京，B Y NewsRx记者，研究称，“发生井下高频扭振(HFTO)会导致钻具严重损坏并且会对钻井效率产生不利影响。因此,建立可靠的HFTO辨识模型是十分必要的至关重要。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews - Researchers detail new data in Support Vector Mac hines. According to news originating fromBeijing, People’s Republic of China, b y NewsRx correspondents, research stated, “The occurrence ofdownhole high-frequ ency torsional oscillations (HFTO) can lead to the significant damage of drillin g toolsand can adversely affect drilling efficiency. Therefore, establishing a reliable HFTO identification model iscrucial.”
Key words
Beijing/People’s Republic of China/Asi a/Algorithms/Emerging Technologies/Machine Learning/Support Vector Machines/Vector Machines/Beijing University
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