利用圆盘球形模糊 Schweizer-Sklar 聚合模型为制造过程选择物联网支持的可持续实时监控策略

IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Shahzaib Ashraf , Muhammad Naeem , Wania Iqbal , Hafiz Muhammad Athar Farid , Hafiz Muhammad Shakeel , Vladimir Simic , Erfan Babaee Tırkolaee
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引用次数: 0

摘要

用于实时监控的物联网(IoT)的出现,旨在通过控制能源损耗实现可持续能源消耗。当前物联网实时监控系统的巨大潜力为未来开发具有环保传感功能的监控设备铺平了道路。因此,创建有效的物联网实时监控设备以减少能源损耗变得至关重要。该建模程序属于多属性群体决策(MAGDM)的范畴,旨在将 Schweizer-Sklar (SS) τ 规范和 τ 规范整合到圆盘球形模糊(D-SF)框架中。目的是增强 D-SF 在处理复杂和不确定数据时的灵活性。这项研究的核心重点是推导出 D-SF 数据的 SS τ 准则和 τ 准则,从而引入创新的聚合算子。文章系统地介绍了使用 SS 聚合算子的基本 D-SF 操作,并提供了详尽的定理说明。文章还介绍了一种新的 MAGDM 工具,该工具利用建议的算子管理模糊和不精确的数据。我们的模型专为解决物联网实时监控系统中减少能量损失这一关键问题而设计。这项研究不仅关注模型的准确性,还强调了模型在解决这一紧迫问题方面的有效性,展示了在可持续能源实践方面取得的重大进展。此外,还对提出的聚合算子进行了比较分析。这种全面的比较不仅增强了算子的功效,还强调了它们在现实世界决策场景中的相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Selection of Internet of Things-enabled sustainable real-time monitoring strategies for manufacturing processes using a disc spherical fuzzy Schweizer–Sklar aggregation model
The emergence of the Internet of Things (IoT) for monitoring in real-time is geared towards sustainable energy consumption practices by taking control over energy loss. The promising potential of current IoT real-time monitoring systems paves the way for future developments in monitoring devices with eco-friendly sensing capabilities. As a result, the creation of effective IoT real-time monitoring devices targeted at decreasing energy loss becomes crucial. This modeling procedure falls under the realm of multiple-attribute group decision-making (MAGDM), aiming to integrate the Schweizer–Sklar (SS) τ-norm and τ-conorm within the disc spherical fuzzy (D-SF) framework. The objective is to enhance the flexibility of D-SF in dealing with intricate and uncertain data. The core focus of this research is on deriving SS τ-norm and τ-conorm for D-SF data, consequently introducing innovative aggregation operators. The article offers the fundamental D-SF operations using SS aggregation operators in a systematic manner, with thorough theorem justifications. A new MAGDM tool is presented, created simply to manage ambiguous and imprecise data utilizing the suggested operators. Our model is specifically designed to tackle the critical issue of reducing energy loss in IoT real-time monitoring systems. The research not only focuses on model accuracy but also emphasizes its effectiveness in solving this pressing problem, demonstrating significant advancements in sustainable energy practices. Moreover, the proposed aggregation operators are subjected to a comparative analysis. This comprehensive comparison not only enhances the operators’ efficacy but also underscores their relevance in real-world decision-making scenarios.
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来源期刊
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence 工程技术-工程:电子与电气
CiteScore
9.60
自引率
10.00%
发文量
505
审稿时长
68 days
期刊介绍: Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.
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