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Study and Application of RSSI-based Wi-Fi Channel Detection Using CNN and Frequency Band Characteristics
Junhyun Park, Hyungho Byun, Chong-Kwon Kim
http://doi.org/10.5626/JOK.2020.47.3.335
For mobile devices, Wi-Fi channel scanning is essential to initiating an internet connection, which enables access to a variety of services, and maintaining a stable link quality by periodic monitoring. However, inefficient Wi-Fi operation, where all channels are scanned regardless of whether or not an access point (AP) exists, wastes resources and leads to performance degradation. In this paper, we present a fast and accurate Wi-Fi channel detection method that learns the dynamic frequency band characteristics of signal strengths collected via a low power antenna using a convolution neural network (CNN). Experiments were conducted to demonstrate the channel detection accuracy for different AP combination scenarios. Furthermore, we analyzed the expected performance gain if the suggested method were to assist the scanning operation of the legacy Wi-Fi.
Opinion Classification in Professional Sports Fan Sites using Topic Keyword-Based Sentiment Analysis
Hyungho Byun, Sihyun Jeong, Chong-kwon Kim
http://doi.org/10.5626/JOK.2018.45.4.390
In this study, we propose the classification method using topic keyword-based sentiment analysis through the posts of professional sports fan sites in Korea. We studied ways to take into account the use of special communication methods or vocabulary in the community and defined keywords based on the characteristics of the topic or frequency of the community"s words. In addition, we presented a new sentiment analysis approach that utilizes the use of keyword pools and the proximity relation to keywords. Through three years of actual community dataset, sentiment analysis based on the topic keyword is more effective than the existing method and reflects the community environment.
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