• Journal of Broadcast Engineering
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• v.26 no.3
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• pp.296-307
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• 2021

A digital hologram (DH) is an ultra-high value-added video content that includes 3D information in 2D data. Therefore, its intellectual property rights must be protected for its distribution. For this, this paper proposes a watermarking method of DH using a deep neural network. This method is a watermark (WM) invisibility, attack robustness, and blind watermarking method that does not use host information in WM extraction. The proposed network consists of four sub-networks: pre-processing for each of the host and WM, WM embedding watermark, and WM extracting watermark. This network expand the WM data to the host instead of shrinking host data to WM and concatenate it to the host to insert the WM by considering the characteristics of a DH having a strong high frequency component. In addition, in the training of this network, the difference in performance according to the data distribution property of DH is identified, and a method of selecting a training data set with the best performance in all types of DH is presented. The proposed method is tested for various types and strengths of attacks to show its performance. It also shows that this method has high practicality as it operates independently of the resolution of the host DH and WM data.

• The korean journal of orthodontics
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• v.27 no.1
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• pp.91-103
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• 1997

This paper aimed to study what the influences of orthodontic treatment of pronunciation are. We compared the duration and the acoustic wave patterns of Korean consonants pronounced by a control group with those of a patient who had his four premolars extracted and had been given orthodontic treatment The results were as follows : 1. Compared to the control group, the treatment group had a longer duration time of consonant pronunciation for all consonants but "ㅅ(s)" and "ㅌ($(t^h)$" in CV(consonant-vowel) pairs. Especially in the case of "ㅈ(dz)", "ㅆ$({\varphi}^h)$" for CV-pairs, and "ㄷ(d)" in VCV(vowel-consonant-vowel) clusters, the duration of consonant sound showed a sharp contrast between the control group and the treatment group. 2. There were clear differences in the acoustic wave patterns of "ㅉ(ts)", "ㅆ$({\varphi}^h)$" and "ㅊ$(c^h)$", all of which were in VCV-clusters. The acoustic wave pattern of "ㅉ(ts)", when pronounced by the treatment group, was stronger than the control group's. This phenomenon was most remarkable in the transitive section where the "ㅉ(ts)" sound flowed into the following vowel. When a preceding vowel shifted to the consonant "ㅆ$({\varphi}^h)$", the attack property of the appeared clearly in the acoustic waves of the treament group, while in the control group the starting point of consonart was indistinctive. Consonant duration for the treatment group was longer, and the appearance of a zero crossing point in the acoustic wave was more frequent. In the case of "ㅊ$(c^h)$", the treatment group produced a strong acoustic wave, and the property of aspiration was obvious in it. 3. When the treatment group pronounced "ㄷ(d)" and "ㅈ(dz)" in CV-pairs, the acoustic-wave was similar to that of aspirated "ㅌ$(t^h)$" and "ㅊ$(c^h)$". 4. The aspirated "ㅌ$(t^h)$" and "ㅊ$(c^h)$" pronounced by the treatment group showed the stronger airstream and acoustic wave form.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.155-174
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• 2022

From January 2020 to October 2021, more than 500,000 academic studies related to COVID-19 (Coronavirus-2, a fatal respiratory syndrome) have been published. The rapid increase in the number of papers related to COVID-19 is putting time and technical constraints on healthcare professionals and policy makers to quickly find important research. Therefore, in this study, we propose a method of extracting useful information from text data of extensive literature using LDA and Word2vec algorithm. Papers related to keywords to be searched were extracted from papers related to COVID-19, and detailed topics were identified. The data used the CORD-19 data set on Kaggle, a free academic resource prepared by major research groups and the White House to respond to the COVID-19 pandemic, updated weekly. The research methods are divided into two main categories. First, 41,062 articles were collected through data filtering and pre-processing of the abstracts of 47,110 academic papers including full text. For this purpose, the number of publications related to COVID-19 by year was analyzed through exploratory data analysis using a Python program, and the top 10 journals under active research were identified. LDA and Word2vec algorithm were used to derive research topics related to COVID-19, and after analyzing related words, similarity was measured. Second, papers containing 'vaccine' and 'treatment' were extracted from among the topics derived from all papers, and a total of 4,555 papers related to 'vaccine' and 5,971 papers related to 'treatment' were extracted. did For each collected paper, detailed topics were analyzed using LDA and Word2vec algorithms, and a clustering method through PCA dimension reduction was applied to visualize groups of papers with similar themes using the t-SNE algorithm. A noteworthy point from the results of this study is that the topics that were not derived from the topics derived for all papers being researched in relation to COVID-19 (