拖车生活实验室中的多视场拓扑孪生体:全体会议

N. Kubota
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引用次数: 0

摘要

近年来,通过信息、智能、通信和机器人技术的融合,人们开始讨论网络物理系统、数字转换和数字孪生,以实现对人服务的数字化。拓扑结构有助于从给定或测量的大数据中提取特征,并在这样的网络世界中模拟真实世界的现象。因此,我们提出了拓扑孪生的概念。拓扑孪生的目的是:(1)提取隐藏在现实世界中的隐式拓扑结构,(2)在网络世界中显式地再现它们,(3)在网络世界中模拟和分析现实世界。拓扑孪生从多视点的角度提取和连接隐藏在现实世界中的结构起着重要的作用。在这次演讲中,我们讨论了复杂服务的拓扑孪生概念,以便从多视角的角度弥合信息物理鸿沟。首先,我们讨论了拖车生活实验室作为一种新型智能家居在未来社会中的作用。我们可以将拖车式生活实验室带入养老院、医院、公共空间,并以多方利益相关者的方式,探讨利用日常生活环境进行开放式创新的共同创造。接下来,我们解释了各种类型的拓扑映射方法、无监督学习方法和基于图的方法作为拓扑智能的方法论。其中之一是生长神经气体(GNG),它可以动态改变由节点和边组成的拓扑结构。我们提出了多种基于多尺度批量学习GNG的方法,称为Fast GNG。接下来,我们展示了Fast GNG与其他方法的比较结果。此外,我们还展示了在拖车生活实验室中进行的多视野拓扑双胞胎的实验结果。最后,讨论了多视域拓扑孪晶的未来研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multiscopic Topological Twin in Trailer Living Laboratory : Plenary Talk
Recently Cyber-Physical Systems, Digital Transformation, and Digital Twin have been discussed towards the digitalization of service to people thanks to the integration of information, intelligence, communication, and robot technologies. topological structure is useful to extract features from given or measured big data and to simulate a real-world phenomenon in such a cyber world. Therefore, we proposed the concept of topological twin. The aim of topological twin is to (1) extract topological structures hidden implicitly in the real world, (2) reproduce them explicitly in the cyber world, and (3) simulate and analyze the real world in the cyber world. The topological twin plays the important role in extracting and connecting structures hidden in real world from the mutliscopic point of view. In this talk, we discuss the concept of topological twin for sophisticated service to people in order to bridge the cyber-physical gap from the multiscopic point of view. First, we discuss the role of trailer living laboratory as a new style of smart home in the future society. We can bring the trailer living laboratory to elderly houses, hospitals, and public spaces, and discuss the co-creation towards open innovation using daily life settings with multi-stakeholder approach. Next, we explain various types of topological mapping methods, unsupervised learning methods, and graph-based methods as the methodology of topological intelligence. One of them is Growing Neural Gas (GNG) that can dynamically change the topological structure composed of nodes and edges. We have proposed various types of methods based on multi-scale batch-learning GNG called Fast GNG. Next, we show the comparison result of Fast GNG with other methods. Furthermore, we show several experimental results of multiscopic topological twin in the trailer living laboratory. Finally, we discuss the future direction of researches on the multiscopic topological twin.
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