Designing Possibilistic Information Fusion Systems: Redundancy as Criterion for Fusion Topologies
暫譯: 設計可能性資訊融合系統：以冗餘作為融合拓撲的標準

Holst, Christoph-Alexander

Designing Possibilistic Information Fusion Systems: Redundancy as Criterion for Fusion Topologies
  • 出版商: Springer
  • 出版日期: 2026-01-03
  • 售價: $5,390
  • 貴賓價: 9.5 折 $5,121
  • 語言: 英文
  • 頁數: 221
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 3032106958
  • ISBN-13: 9783032106957
  • 相關分類: Machine Learning

    • 海外代購書籍(需單獨結帳)

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商品描述

Intelligent technical systems process information from multiple sources, but are confronted with uncertainties inherent in the information which is often imprecise, incomplete, or inconsistent. As the number of information sources increases, so does the uncertainty, as well as the risk that individual sources are unreliable. This leads to a lack of confidence in analyses and decisions. This thesis presents the Redundancy-hardened Robust Fusion System (R2FS), which aims to exploit redundancies in information sources to increase robustness against changes in source reliability. Leveraging the strengths of possibility theory, it identifies redundancies in information sources, even in environments where information is scarce and characterised by a high degree of epistemic uncertainty. Based on the novel dual redundancy metric proposed in this thesis, redundant sources are aligned in a distributed fusion topology. It is demonstrated that the R2FS outperforms established possibilistic fusion rules in terms of robustness due to the exploitation of redundancy in the distributed topology. This book concludes with a discussion of the current state of uncertainty modelling, highlighting how uncertainty modelling techniques currently used in information fusion could benefit machine learning applications.

商品描述(中文翻譯)

智能技術系統處理來自多個來源的信息，但面臨著信息固有的不確定性，這些信息往往是不精確、不完整或不一致的。隨著信息來源的數量增加，不確定性也隨之增加，個別來源不可靠的風險也隨之上升。這導致對分析和決策缺乏信心。本論文提出了冗餘加固的穩健融合系統（Redundancy-hardened Robust Fusion System, R2FS），旨在利用信息來源中的冗餘性來提高對來源可靠性變化的穩健性。通過利用可能性理論的優勢，它能夠識別信息來源中的冗餘，即使在信息稀缺且具有高度認知不確定性的環境中也是如此。基於本論文提出的新型雙冗餘度量，冗餘來源在分佈式融合拓撲中進行對齊。實驗表明，R2FS在穩健性方面超越了既有的可能性融合規則，這得益於在分佈式拓撲中對冗餘的利用。本書最後討論了當前不確定性建模的狀態，強調了目前在信息融合中使用的不確定性建模技術如何能夠惠及機器學習應用。

作者簡介

Dr.-Ing. Christoph-Alexander Holst is member of the executive board of the Institute Industrial IT (inIT) and research group manager of the Discrete Systems working group (image processing and pattern recognition, sensor and information fusion). He completed his master's degree in the international programme Information Technology at the Ostwestfalen-Lippe University of Applied Sciences and Arts. He received his doctoral degree (Dr.-Ing.) from the Brandenburg University of Technology Cottbus-Senftenberg. His research focuses on methods of information fusion, uncertainty modelling and machine learning systems in the context of resource-constrained systems and scarce data.

作者簡介(中文翻譯)

Dr.-Ing. Christoph-Alexander Hols是工業資訊研究所（Institute Industrial IT, inIT）執行董事會成員及離散系統工作組（影像處理與模式識別、感測器與資訊融合）研究小組經理。他在奧斯特法倫-利佩應用科技大學（Ostwestfalen-Lippe University of Applied Sciences and Arts）完成了國際資訊科技碩士學位。他在布蘭登堡科技大學科特布斯-森芬特貝格（Brandenburg University of Technology Cottbus-Senftenberg）獲得博士學位（Dr.-Ing.）。他的研究專注於在資源受限系統和數據稀缺的背景下，資訊融合方法、不確定性建模和機器學習系統。

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