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Existing imputation algorithms for incomplete decision system are almost non-incremental and rarely consider different contribution to decision label among different features. Therefore, in order to make the most use of information hidden in existing data and reserve the original distribution characteristics, in this paper, we proposes a new incremental imputation algorithm based on attribute significance...
The switching power supply is widely used in many applications, but due to its complexity, it is difficult to solve the fault diagnosis problems by the traditional fault diagnosis methods. And many algorithms based on data driven, offer a tool to deal with these problems without any preliminary or additional information. However, the incomplete data (or missing values) deteriorate their performance...
Being able to recognize human activities is essential for several applications, including personal assistive robotics and smart homes. In this paper, we perform the recognition of the human activity based on the combined SVM&HMM in daily living environments. Firstly, we use a RGBD sensor (Microsoft Kinect) as the input sensor, and extract a set of the fusion features, including motion, body structure...
In many applications of wireless sensor networks, sensor nodes are required to move around to complete their job assignments, e.g., collecting data or exploring the environment. In those cases, the location of sensor nodes is an important information. In previous localization studies, it is required that certain anchor nodes be fixed in locations to provide location references. In this paper, we propose...
A novel approach to multi-robot cooperative map-building in complex environments is presented in this paper. The approach lets all robots operate individually and then tries to merge the different local grid maps into a single global one. Without using any pose information of the robots, the process of map merging is performed by measuring the similarity between grid maps. A distance transform and...
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