Earticle

At a time when securing driving safety is the most important in the development and commercialization of autonomous vehicles, AI and big data-based algorithms are being studied to enhance and optimize the recognition and detection performance of various static and dynamic vehicles. However, there are many research cases to recognize it as the same vehicle by utilizing the unique advantages of radar and cameras, but they do not use deep learning image processing technology or detect only short distances as the same target due to radar performance problems. Radars can recognize vehicles without errors in situations such as night and fog, but it is not accurate even if the type of object is determined through RCS values, so accurate classification of the object through images such as cameras is required. Therefore, we propose a fusion-based vehicle recognition method that configures data sets that can be collected by radar device and camera device, calculates errors in the data sets, and recognizes them as the same target.

As society gradually enters a virtual, non-face-to-face society, the use of online content is increasing as well. In particular, as smartphones are thoroughly established in our daily life, the platforms of webtoons, mobile broadcasting, and education are shifting from personal computers to smartphones. Recently, the development of the Over-The-Top media service (OTT service) enabled streaming services of various media contents through the internet and activation of IPTV. Therefore, the rapid increase of popularity of short-form content is a natural phenomenon with smartphone platforms with fast, improvised, and endless communication. Lately, TikTok became the favored platform with prosumers, defined as people who are both producers and consumers. In this study, I studied the experiential response of YouTube and TikTok users as representative examples of a short-form content platform developed after the 2000s, the flourishing years of digital content with a length of 30 seconds.

Recently, IoT systems are cloud-based, so that continuous and large amounts of data collected from sensor nodes are processed in the data server through the cloud. However, in the centralized configuration of large-scale cloud computing, computational processing must be performed at a physical location where data collection and processing take place, and the need for edge computers to reduce the network load of the cloud system is gradually expanding. In this paper, a cluster system consisting of 6 inexpensive Raspberry Pi boards was constructed to perform fast data processing. And we propose "Kubernetes cluster system(KCS)" for processing large data collection and analysis by model distribution and data pipeline method. To compare the performance of this study, an ensemble model of deep learning was built, and the accuracy, processing performance, and processing time through the proposed KCS system and model distribution were compared and analyzed. As a result, the ensemble model was excellent in accuracy, but the KCS implemented as a data pipeline proved to be superior in processing speed..