Self-Adapting Multi-sensor Systems: A Concept for Self-Improvement and Self-Healing Techniques
Self-Adapting Multi-sensor Systems: A Concept for Self-Improvement and Self-Healing Techniques
复制标题
自适应多传感器系统：自我改进和自我修复技术的概念
DOI：
10.1109/sasow.2014.22
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
D. Bannach
中科院分区：
文献类型：
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作者：
Martin Jänicke;B. Sick;P. Lukowicz;D. Bannach
活动识别（AR）系统越来越多地进入我们的日常生活，从监控日常活动到支持医疗保健。然而，这样的系统倾向于与狭义定义的规范一起使用，要求其用户进行依赖于应用的设置和配置。一个长期目标是自治系统，能够在没有（或最少）用户交互的情况下工作。与该愿景密切相关的是自主添加进一步输入源的能力（例如，传感器），导致输入空间的维数增加。我们的方法旨在系统地研究创建自适应分类系统所需的方法。这包括一个架构，基于有机计算（OC）的原则，以及发展的措施比较概率模型和程序评估不同维度的分类。有了这样的评估技术，系统应该能够在运行时以自组织的方式调整其系统模型。除了自我改进（添加新的传感器），我们还解决了自我修复（更换掉电的传感器）的问题。
Activity Recognition (AR) Systems more and more find their way into our daily lives, from monitoring daily activities to support in medical care. However, such systems tend to be used with narrowly defined specifications, demanding for application-dependent setup and configuration by their users. A long term goal are autonomous systems, being able to work with no (or minimal) user interaction. Closely related to that vision is the ability of autonomously adding further input sources (e.g., sensors) at run-time, leading to an increased dimensionality of the input-space. Our approach aims at systematically investigating methods necessary for the creation of self-adapting classification systems. This includes an architecture, based on Organic Computing (OC) principles, as well as the development of measures for comparing probabilistic models and procedures for evaluating classifiers of different dimensionality. With such evaluation techniques, systems should be able to adapt their system model at run-time in a self-organized manner. Besides self-improvement (adding a new sensor) we also address the problem of self-healing (replacing a sensor that dropped out).
“认识你自己”——智能技术系统中的计算自我反思
DOI：
10.1109/sasow.2014.25
发表时间：
2014
期刊：
2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops
影响因子：
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作者：
Sven Tomforde;Jorg Hahner;Sebastian von Mammen;Christian Gruhl;Bernhard Sick;Kurt Geihs
通讯作者：
Kurt Geihs