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首页> 外文期刊>Expert systems with applications >A cross-disciplinary comparison of multimodal data fusion approaches and applications: Accelerating learning through trans-disciplinary information sharing
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A cross-disciplinary comparison of multimodal data fusion approaches and applications: Accelerating learning through trans-disciplinary information sharing

机译：多式数数据融合方法和应用的跨学科比较：通过跨学科信息共享加速学习

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Multimodal data fusion (MMDF) is the process of combining disparate data streams (of different dimensionality, resolution, type, etc.) to generate information in a form that is more understandable or usable. Despite the explosion of data availability in recent decades, as yet there is no well-developed theoretical basis for multimodal data fusion, i.e., no way to determine a priori which approach is best suited to combine an arbitrary set of available data to achieve a stated goal for a given application. This has resulted in exploration of a wide variety of approaches across numerous domains but as yet very little integration of conclusions at a meta (cross-disciplinary) level. In response, this manuscript poses the following questions: (1) How convergent (or divergent) are approaches within single disciplines? (2) How similar are the challenges posed across different disciplines, i.e., might there be opportunity for successes in MMDF achieved in one field to inform progress in other areas as well? and (3) Where are the outstanding gaps in MMDF research, and what does this imply as targets for high impact research in the coming years? To begin to answer these questions, an apples-to-apples comparison of the literature of nine stakeholder-centric engineering domains (civil engineering, transportation, energy, environmental engineering, food engineering, critical care (healthcare), neuroscience, manufacturing/automation, and robotics) was created by quantifying the numbers and dimensionalities of modalities and sensors in each published project and classifying the algorithms used and purposes for which they are used. Within disciplines, it is shown there is often a tendency for use of similar methodologies, both in choice of level of fusion and data algorithm class. Yet this analysis also reveals that many problem types (defined by data dimensionality, modality number and type, and fusion purpose) are shared across different domains and are approached differently in those domains, e.g., transportation problems have similar characteristics to critical care, food science, robotics, and civil engineering. Of the disciplines studied, most ( 75%) share problem characteristics with 3-5 others; to support leveraging these resources, lookup tables indexed by data dimensions, number of modalities, etc. are provided as a starting point for cross-disciplinary MMDF literature searches for new applications. Critical gaps identified are (1) a drop off of the number of published studies with increasing number of distinct modalities and (2) a dearth of publications tackling challenges with high dimensionality inputs, especially time-series 2D and 3D data. These gaps may point to topics where algorithm development will be fruitful to enable future solutions as video and other high-dimensionality sensors decrease in price. Finally, the lack of a shared vocabulary across disciplines makes analyses like the one conducted here challenging, as does the often implicit incorporation of expert knowledge into design; therefore progress toward a better leveraging of the current state of knowledge and toward a theoretical MMDF framework depends critically on improved cross-disciplinary communication and coordination on this topic.
机译：多模式数据融合（MMDF）是组合不同数据流（不同维度，分辨率，类型等）的过程，以以更易于理解或可用的形式生成信息。尽管近几十年来数据可用性爆炸，但对于多式联数据融合没有良好开发的理论基础，即无法确定先验的方法，该方法最适合将任意集合的可用数据组合以实现陈述给定申请的目标。这导致探讨了众多域中各种各样的方法，但在META（跨学科）水平上的结论很少融合。作为响应，此稿件造成以下问题：（1）收敛（或发散）如何在单一学科内接近？ （2）在不同学科中提出的挑战是如何相似的，即，可能会在一个领域取得的MMDF成功的机会，以便在其他领域提供信息？ （3）MMDF研究中的出色差距在哪里，这意味着未来几年的高影响力研究的目标是什么？要开始回答这些问题，苹果到苹果的苹果对比较的九个利益攸关方为中心的工程领域（土木工程，运输，能源，环境工程，食品工程，关键护理（医疗保健），神经科学，制造/自动化，通过量化每个已发布的项目中的方式和传感器的数量和尺寸以及分类所使用的算法和使用它们的算法来创建和机器人。在学科中，它显示出通常在选择融合和数据算法类别中使用类似方法的趋势。然而，该分析还揭示了许多问题类型（由数据维度，模态数和类型和融合目的定义）在不同的域中共享，并且在这些域中的不同方式接近，例如，运输问题具有与重大关心的特征相似，食品科学，机器人和土木工程。学科学科，大多数（> 75％）与其他3-5人分享问题特征;为了支持利用这些资源，通过数据维度索引的查找表，模当数量等作为跨学科MMDF文献搜索新应用的起点。确定的关键差距是（1）随着越来越多的方式和（2）缺乏对高维输入，特别是时间序列2D和3D数据的挑战，所公布的研究数量的数量和（2）缺乏出版物。这些差距可能指向算法开发将富有成效的主题，以使未来解决方案能够作为视频和其他高维传感器的价格下降。最后，缺乏跨学科的共同词汇量，就像在这里挑战的那样分析，往往将专家知识的常规纳入设计中的常规;因此，更好地利用当前知识状态和理论上的MMDF框架的进展既批判性地依赖于改善本主题的跨学科沟通和协调。

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