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<p><strong>Abstract.</strong> Outdoor stone cultural properties are continuously affected by the external environment such as wind, rain, and earthquakes. These cause damage to the cultural properties by not only threatening structural stability but also damaging the aesthetic value. Quick detection of these damages is important to enable appropriate preservation treatment in terms of cultural property conservation management. Even though conventional manual damage detection methods are widely used, they are limited by manpower, cost, and other external conditions. In this paper, we propose a system that automatically detects and classifies damage occurring in cultural properties using deep-learning technique to settle these drawbacks. In detail, the damages are classified into four types (i.e., crack, loss, detachment, biological colonization) based on Faster region-based convolutional neural network (R-CNN) algorithm. In addition, we construct an image dataset of stone damage, which is collected by the regular report of the National Designated Cultural Property in 2017 conducted by the Cultural Heritage Administration of S. Korea, and augment its dataset to enhance damage detection performance. From the experiment conducted, we achieved an average confidence score of 94.6&amp;thinsp;% or more on the 20 test images.</p>

Recently, smart mobile devices and wireless communication technologies such as WiFi, third generation (3G), and long-term evolution (LTE) have been rapidly deployed. Many smart mobile device users can access the Internet wirelessly, which has increased mobile traffic. In 2014, more than half of the mobile traffic around the world was devoted to satisfying the increased demand for the video streaming. In this paper, we propose a scalable video streaming relay scheme. Because many collisions degrade the scalability of video streaming, we first separate networks to prevent excessive contention between devices. In addition, the member device controls the video download rate in order to adapt to video playback. If the data are sufficiently buffered, the member device stops the download. If not, it requests additional video data. We implemented apps to evaluate the proposed scheme and conducted experiments with smart mobile devices. The results showed that our scheme improves the scalability of video streaming in a wireless local area network (WLAN).

In this paper, we propose mobile ad hoc network configuration and its autonomous network construction method for efficiency and scalability of media streaming for mobile smart devices. To provide scalable network configuration for streaming traffic distribution, an IEEE 802.11 infrastructure network and ad hoc networks are hierarchically built. The proposed method autonomously configures a hierarchical streaming network by competition based on performance and states of devices and the wireless network, not depending on any specific nodes. Finally, we conduct performance measurement for the proposed configuration and analyze the experimental result.