开发基于物联网的概率模型,快速识别海运港口作业中隐藏的放射性物质,加强全球供应链安全

S. Jakovlev, Tomas Eglynas, Mindaugas Jusis, Miroslav Voznak
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引用次数: 0

摘要

发现隐藏的放射性材料仍然是全球供应链中的一大障碍。最近的研究尚未充分调查基于物联网(IoT)的实用方法,以改进和实施高效的数据融合技术。当前的系统经常滥用资源,导致典型环境下的安全漏洞。我们的研究深入探讨了使用单传感器和多传感器配置进行检测的基本原理,并采用概率方法进行数据融合。我们引入了一个模型,旨在加速检测实际港口作业中的辐射排放。研究结果凸显了该模型在快速识别方面的有效性,并确定了在涉及堆叠集装箱的情况下应用该模型的最佳条件,无论这些集装箱是在船上还是放置在仓储区。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Developing an IoT-enabled probabilistic model for quick identification of hidden radioactive materials in maritime port operations to strengthen global supply chain security
Uncovering hidden radioactive materials continues to be a major hurdle in worldwide supply chains. Recent research has not adequately investigated practical Internet of Things (IoT)-based approaches for improving and implementing efficient data fusion techniques. Current systems often misuse resources, leading to security vulnerabilities in typical settings. Our research delves into the fundamental principles of detection using both single and multiple sensor configurations, adopting a probabilistic method for merging data. We introduce a model aimed at accelerating the detection of radiation emissions in actual port operations. The results highlight the model’s effectiveness in rapid identification and determine the best conditions for its application in scenarios involving stacked containers, whether they are on ships or positioned in storage areas.
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