School of Automation, Xi'an University of Posts and Telecommunications, Xi'an 710121, China.
School of Automation, Central South University, Changsha 410083, China.
Sensors (Basel). 2019 Sep 25;19(19):4146. doi: 10.3390/s19194146.
In most of the application scenarios of industrial control systems, the switching threshold of a device, such as a street light system, is typically set to a fixed value. To meet the requirements for a smart city, it is necessary to set a threshold that is adaptive to different conditions by fusing the multi-attribute observations of the sensors. This paper proposes a multi-attribute fusion algorithm based on fuzzy clustering and improved evidence theory. All of the observations are clustered by fuzzy clustering, where a proper clustering method is chosen, and the improved evidence theory is used to fuse the observations. In the experiments, two-dimensional observations for the street light illumination and for the ambient illumination are used in a campus-intelligent lighting system based on a narrowband Internet of things, and the results demonstrate the effectiveness of the proposed fusion algorithm. The proposed algorithm can be applied to a variety of multi-attribute fusion scenarios.
在大多数工业控制系统的应用场景中，设备的切换阈值（如路灯系统）通常被设置为固定值。为了满足智慧城市的要求，需要通过融合传感器的多属性观测值来设置适应不同条件的阈值。本文提出了一种基于模糊聚类和改进证据理论的多属性融合算法。所有观测值都通过模糊聚类进行聚类，选择合适的聚类方法，并使用改进的证据理论融合观测值。在实验中，基于窄带物联网的校园智能照明系统使用二维光照观测值和环境光照观测值，实验结果证明了所提出的融合算法的有效性。所提出的算法可以应用于多种多属性融合场景。
