工作场所风险和灾害管理的深度分析

IF 1.3 4区 计算机科学 Q1 Computer Science
S. Dalal;D. Bassu
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引用次数: 1

摘要

我们讨论了多模态数据融合的动态实时分析,用于上下文风险识别,为工作场所生成“风险图”,从而及时识别危害并减轻相关风险。它包括新的机器/深度学习、分析、方法及其应用程序,这些应用程序处理从图片、视频、文档、移动应用程序、传感器/物联网、职业安全与健康管理局(OSHA)规则和建筑信息模型(BIM)模型收集的非常规数据。具体来说,我们通过计算机视觉、自然语言处理和传感器数据分析的应用,描述了该领域的一些进展和挑战。应用程序包括使用当前和历史索赔数据以及其他公共数据的自动原因识别、损害预防和灾难恢复。所开发的方法可以应用于任何特定情况下的不同人群,包括第一响应者。最后,我们将讨论与业务实用性、隐私和行业法规相关的一些重要的非技术挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep analytics for workplace risk and disaster management
We discuss dynamic real-time analysis from multimodal data fusion for contextual risk identification to generate “risk maps” for the workplace, resulting in timely identification of hazards and associated risk mitigation. It includes new machine/deep learning, analytics, methods, and its applications that deal with the unconventional data collected from pictures, videos, documents, mobile apps, sensors/Internet of Things, Occupational Safety and Health Administration (OSHA) rules, and Building Information Model (BIM) Models. Specifically, we describe a number of advances and challenges in this field with applications of computer vision, natural language processing, and sensor data analysis. Applications include automated cause identification, damage prevention, and disaster recovery using current and historical claims data and other public data. The methods developed can be applied to any given situation with different groups of people, including first responders. Finally, we discuss some of the important nontechnical challenges related to business practicality, privacy, and industry regulations.
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来源期刊
IBM Journal of Research and Development
IBM Journal of Research and Development 工程技术-计算机:硬件
自引率
0.00%
发文量
0
审稿时长
6-12 weeks
期刊介绍: The IBM Journal of Research and Development is a peer-reviewed technical journal, published bimonthly, which features the work of authors in the science, technology and engineering of information systems. Papers are written for the worldwide scientific research and development community and knowledgeable professionals. Submitted papers are welcome from the IBM technical community and from non-IBM authors on topics relevant to the scientific and technical content of the Journal.
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