使用机器学习保护工业中的危险场所

Praveen Sankarasubramanian
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引用次数: 0

摘要

在处理不同行业的有毒废物、放射性物质、化学原料、化学废物和生物制品时,必须采取极端的预防措施。危险交通网络中的任何故障都可能导致严重事故、死亡和/或严重损害。直接监测和分析,并采取预防措施防止故障的蔓延,可以显著减少不良影响的再次发生。目前的研究表明,对管道监测和研究的最新发展进行详细的宣传和信息可能有助于未来石油工业的现代化。我们还提出了一个及时检测管道泄漏的框架,特别是在石油和天然气领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Protection of Hazardous Places in Industries using Machine Learning
Extreme precautions must be observed to handle toxic wastes, radioactive substances, chemical raw materials, chemical wastes, and bio-products in different industries. Any malfunction in a dangerous traffic network can lead to serious accidents, deaths and / or serious damage. Direct monitoring and analysis, and preventive measures to prevent the spread of failures, can significantly reduce the recurrence of adverse effects. Current research suggests that detailed publicity and information on the latest developments in pipeline monitoring and research may help modernize the oil industry in the future. We also propose a framework to detect timely leakage in pipelines, especially in oil and gas sector.
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