Research on Intelligent Embedded Fan System Based on YOLOv2 Lightweight Algorithm.

With the development of artificial intelligence, the application of intelligent algorithms to low-power embedded chips has become a new research topic today. Based on this, this study optimizes the YOLOv2 algorithm by tailoring and successfully deploys it on the K210 chip to train the face object detection algorithm model separately. The intelligent fan with YOLOv2 model deployed in K210 chip can detect the target of the character and obtain the position and size of the character in the machine coordinates. Based on the obtained information of character coordinate position and size, the fan's turning Angle and the size of air supply are intelligently perceived. The experimental results show that the intelligent fan design method proposed here is a new embedded chip intelligent method of cutting and improving the YOLOv2 algorithm. It innovatively designed solo tracking, crowd tracking, and intelligent ranging algorithms, which perform well in human perception of solo tracking and crowd tracking and automatic air volume adjustment, improve the accuracy of air delivery and user comfort, and also provide good theoretical and practical support for the combination of AI and embedded in other fields.

Geographical Topics Learning of Geo-Tagged Social Images.

With the availability of cheap location sensors, geotagging of images in online social media is very popular. With a large amount of geo-tagged social images, it is interesting to study how these images are shared across geographical regions and how the geographical language characteristics and vision patterns are distributed across different regions. Unlike textual document, geo-tagged social image contains multiple types of content, i.e., textual description, visual content, and geographical information. Existing approaches usually mine geographical characteristics using a subset of multiple types of image contents or combining those contents linearly, which ignore correlations between different types of contents, and their geographical distributions. Therefore, in this paper, we propose a novel method to discover geographical characteristics of geo-tagged social images using a geographical topic model called geographical topic model of social images (GTMSIs). GTMSI integrates multiple types of social image contents as well as the geographical distributions, in which image topics are modeled based on both vocabulary and visual features. In GTMSI, each region of the image would have its own topic distribution, and hence have its own language model and vision pattern. Experimental results show that our GTMSI could identify interesting topics and vision patterns, as well as provide location prediction and image tagging.