• Journal of the Korean Society of Radiology
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• v.15 no.2
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• pp.153-158
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• 2021

X-ray image analysis is a very important field to improve the early diagnosis rate and prediction accuracy of periodontal disease. Research on the development and application of artificial intelligence-based algorithms to improve the quality of such dental X-ray images is being widely conducted worldwide. Thus, the aim of this study was to design a super-resolution algorithm for predicting periodontal disease and to evaluate its applicability in dental X-ray images. The super-resolution algorithm was constructed based on the convolution layer and ReLU, and an image obtained by up-sampling a low-resolution image by 2 times was used as an input data. Also, 1,500 dental X-ray data used for deep learning training were used. Quantitative evaluation of images used root mean square error and structural similarity, which are factors that can measure similarity through comparison of two images. In addition, the recently developed no-reference based natural image quality evaluator and blind/referenceless image spatial quality evaluator were additionally analyzed. According to the results, we confirmed that the average similarity and no-reference-based evaluation values were improved by 1.86 and 2.14 times, respectively, compared to the existing bicubic-based upsampling method when the proposed method was used. In conclusion, the super-resolution algorithm for predicting periodontal disease proved useful in dental X-ray images, and it is expected to be highly applicable in various fields in the future.

• Information Systems Review
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• v.21 no.1
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• pp.23-38
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• 2019

The social network service (SNS) is one of the important marketing channels, so many companies actively exploit SNSs by posting SNS messages with appropriate content and style for their customers. In this paper, we focused on the psychological distances embedded in the SNS messages and developed a method to measure the psychological distance in SNS message by mixing a traditional content analysis, natural language processing (NLP), and machine learning. Through a traditional content analysis by human coding, the psychological distance was extracted from the SNS message, and these coding results were used for input data for NLP and machine learning. With NLP, word embedding was executed and Bag of Word was created. The Support Vector Machine, one of machine learning techniques was performed to train and test the psychological distance in SNS message. As a result, sensitivity and precision of SVM prediction were significantly low because of the extreme skewness of dataset. We improved the performance of SVM by balancing the ratio of data by upsampling technique and using data coded with the same value in first content analysis. All performance index was more than 70%, which showed that psychological distance can be measured well.

• Journal of IKEEE
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• v.15 no.2
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• pp.179-189
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• 2011

This paper presents an SVC based multi-channel transmission technique. Transmission of high definition multimedia and its service efficiency can be significantly improved by the proposed method. In this method, the HD stream is divided into the two layer streams, i.e., a base layer stream and an enhancement layer stream. The divided streams are transmitted through a primary channel and an auxiliary channel, respectively. The proposed technique provides a noble mode switching technique which enables a seamless service of HD multimedia even under the conditions of abrupt and intermittent deterioration of the auxiliary channel. When the enhancement layer stream is disrupted by the channel monitoring in the mode switching algorithm, the algorithm works further to maintain the spatial and time resolution of the HD multimedia by upsampling and interpolating the base layer stream, consequently serving for the non disrupted play of the media. Moreover, the adoption of an adaptive switching algorithm significantly reduces the frequency of channel disruption avoiding the unnecessary switching for the short period variations of the channel. The feasibility of the proposed technique is verified through the simulation study with an example application to the simultaneous utilization of both Ku and Ka bands for HD multimedia broadcasting service. The rainfall modeling and the analysis of the satellite channel attenuation characteristics are performed to simulate the quality of service performance of the proposed HD broadcasting method. The simulation results obtained under a relatively poor channel (weather) situations show that the average lasting period of enhancement layer service is extended from 9.48[min] to 23.12[min] and the average switching frequency is reduced from 3.84[times/hour] to 1.68[times/hour]. It is verified in the satellite example that the proposed SVC based transmission technique best utilizes the Ka band channel for the service of HD broadcasting, although it is characterized by its inherent weather related poor reliability causing severe limitations in its independent application.