본 연구는 디지털 영상 제작 프로세스에서 점점 중요한 역할을 하고 있는 생성형 AI의 발전과 그 영향을 탐구한다. GPT, GAN 및 기타 생성 알고리즘과 같은 모델의 개발에서 AI 기술의 급속한 발전으로 영상 제작 환경이 큰 변화를 겪고 있다. ChatGPT, Runway, DALL·E, MidJourney, SunoAI 등 생성형 AI 모델의 발전으로 영상 제작 단계에서 적용 가능성이 크게 확장되었다. 생성형 AI는 아이디어 기획에서부터 최종 편집 프로세스에 이르기까지 다양한 제 작 단계를 간소화할 수 있는 잠재력이 있다. 예를 들어, AI는 플롯 아이디어나 대화를 생성하 여 대본 작성을 지원할 수 있으며, 후반 작업에서는 시각 효과를 향상시키고, 사실적인 환경을 만들거나, 반복적인 편집 작업을 자동화할 수 있다. 또한, AI 기반의 사운드 디자인 도구는 영 상 분위기에 맞춘 음악과 사운드 효과를 자동으로 생성할 수 있다. 본 연구는 현재 사용되고 있는 생성형 AI 기술을 조사하고, 특히 런웨이 AI 영화제에서 소개된 사례들을 통해 그 장점 과 한계를 분석한다. 연구 결과, 생성형 AI는 영상 제작에 드는 시간과 비용을 획기적으로 줄 일 수 있는 잠재력을 지닌 반면, 저작권 문제와 딥페이크와 같은 기술적, 윤리적 문제는 신중 한 고려가 필요함을 제시한다. 향후 연구 과제는 AI와 인간의 예술적 창의성 간의 균형을 유 지하는 방법에 관한 것이다.

This study explores EFL English learners’ reactions and achievements when exposed to two types of online learning, pre-recorded lectures and real-time lectures, in a semester-long English reading class. The participants were 60 Korean university English learners who attended both types of online lectures for six weeks each. They studied three parts: reading comprehension for top-down and bottom-up reading skills, grammar, and vocabulary. Midterm and final exams assessed the learners’ achievements, and one online survey was conducted to investigate their reactions. The results showed that the participants preferred to study recordings and were most satisfied with their grammar studies in recorded lectures but preferred real-time lectures for reading comprehension. The study also found that they attained better scores on top-down reading skills and vocabulary learning through recorded lessons and higher scores on bottom-up reading skills and grammar learning through real-time lectures. Finally, the study shows the participants preferred to attend a combination of both online methods. This study suggests salient online learning ways for an English reading course.

Degenerative arthritis is a common joint disease that affects many elderly people and is typically diagnosed through radiography. However, the need for remote diagnosis is increasing because knee pain and walking disorders caused by degenerative arthritis make face-to-face treatment difficult. This study collects three-dimensional joint coordinates in real time using Azure Kinect DK and calculates 6 gait features through visualization and one-way ANOVA verification. The random forest classifier, trained with these characteristics, classified degenerative arthritis with an accuracy of 97.52%, and the model's basis for classification was identified through classification algorithm by features. Overall, this study not only compensated for the shortcomings of existing diagnostic methods, but also constructed a high-accuracy prediction model using statistically verified gait features and provided detailed prediction results.

Due to the development of the internet and communications technology, the media market has undergone significant changes in the 2000s. In the past, when the types of media were not diverse, media content was mainly consumed through TV, radio, newspapers, etc., but as varied, it allowed users to watch the content anytime and anywhere. ‘Snack Video’ which is produced in a short length and consumed in commuting time and leisure time, and ‘Snack Culture’ which is a popular lifestyle trend of consuming short-form cultural content, is now widespread. The representative content of snack content is video clips. Various of media provide video clips, such as portal sites, SNS, and TV programs. YouTube is effective in increasing purchases than the other channels. Over-The-Top (OTT) is another pillar of the new media market, which has emerged with the development of the internet and communications technologies.

Measuring service quality and related key dimensions has been an important problem in Marketing. In this research, we would introduce a smart methodological framework to efficiently identify refined, key sentiment dimensions for measuring the service quality using both traditional survey and unstructured online reviews (natural survey). The proposed framework consists of three parts: (1) steps for preprocessing the unstructured reviews to generate attribute-level sentiments for independent variables (2) Bayesian regression to efficiently identify key groups of correlated attributes and (3) post-hoc analysis for identifying dimensions from the selected groups of correlated attributes and predicting dimension-level effects. Note, the first part of the framework (i.e., preprocessing) is not required for analyzing traditional surveys. Our framework provides two sets of complementing results such as attribute-level effects under the identified dimensions and aggregate dimension-level effects. In the first application study to traditional SERVQUAL data, we successfully validated the proposed framework by comparing the results between our framework and three commonly used existing methods of regression, lasso regression, and factor analysis. In the second empirical application study with the online reviews from a major game review website, STEAM platform, we found that our framework provided a significantly reduced number of key dimensions which were surprisingly efficient for predicting and explaining the service quality ratings, compared with the same set of compared methods in the first study plus the topic model. In particular, with reviews of 2,825 games, three key dimensions of Mechanical playability, Fun in fantasy and Money for value were identified, and we also found that the Mechanical playability could be an important driver of game popularity.

As the use of artificial intelligence (AI) grows, so do the questions regarding this new technology and its potential uses. Among the various possibilities and employment that could be offered by AI is personalized news technology. Nowadays, it is already possible to produce journalistic content through AI (Carlson, 2014; Graefe & Haim, 2018). Digital storytelling has become a reality through automated journalism powered by AI (Caswell & Dörr, 2018; Galily, 2018; Linden, 2017; Thorne, 2020). “Artificial intelligence applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take actions” (Gartner Group, 2019). In personalized news technology, algorithms are responsible for selecting content and sorting it according to the personalization criteria (Powers, 2017). So far, AI has been studied in different fields with distinct research focuses (Loureiro et al., 2021). Studies of news-personalization technologies have mainly focused on research engines and filtering mechanisms (Darvishy et al., 2020; Haim et al., 2017; Manoharan & Senthilkumar, 2020). Few studies examine news aggregators (Haim et al., 2018; Kwak et al., 2021) and the effects of news personalization on audiences (Merten, 2021; Swart, 2021; Thurman et al., 2019), thus demanding further research. AI is an imminent reality for the future, reshaping the news media (Brennen et al., 2022; Linden, 2017; Thorne, 2020). Hence, it is still necessary to investigate the impacts that this technology potentially offers to users. Therefore, the current study seeks to respond to this need to deepen research into the area of news personalization through AI, by analyzing the response of audiences toward current and future technological tendencies. The main aim of this research is to investigate the levels of trust that users have in AI-generated personalized video news.

While there is an increasing number of studies highlighting the power of videos in influencing audience attitudes and behavior, academic research in tourism is largely behind due to the methodological challenges of analyzing unstructured video data. This study adopts an automatic video analytics approach to examine the relationship between content features of pro-environmental videos and audience engagement in tourism. Artificial intelligence was used to extract video content features by detecting scenes and shots as well as labels (e.g., trees). Our findings suggest that there exists an inverted U-shape relationship between video informativeness and audience engagement. This study makes significant theoretical and methodological contributions to extant tourism literature by theoretically explaining and empirically testing how video content features influence audience engagement in pro-environmental video communications in tourism.

This study aims to address two important questions: will advertising on mobile short-form video apps jeopardize the value perception of luxury brands (RQ1), and if so, how will self-deprecating online reviews eliminate these negative effects (RQ2). An experimental design approach was employed to investigate the proposed research questions. Three experiments were conducted to test the hypotheses. SPSS was used for data analysis. The study 1 finds that compared with traditional media, advertising on mobile short-form video apps shortened the psychological distance between consumers and luxury, therefore has a more negative impact on consumers’ perception of luxury brands. The study 2 reveals that self-deprecating online reviews can eliminate the negative effects of advertising of luxury brands. On the basis of previous research, this paper proves the negative influence of social media on luxury brands in the scene of new social media-mobile short format video application. In addition, it also studies the moderating effect of online comments, especially self-deprecating comments, on consumers' perception of luxury brands. This study outlines theoretical contributions and practical implications for the luxury marketing management and made suggestions for future research in the field of luxury marketing in Social Media.

본 연구는 360도 파노라마 동영상을 활용하여 해상풍력 경관의 유형을 분류하고 유형별 경관 선호 매 트릭스 요소에 따른 경관 선호도를 분석하고자 한다. 이를 위해 첫째, 연구대상지로 제주 탐라해상풍력 단지로 선정하였으며, 연구대상지를 조망할 수 있는 지점들을 추출하고 방문하여 360도 파노라마 동영 상을 촬영하였다. 둘째, 구축된 360도 파노라마 동영상 자료를 바탕으로 경관의 미학적 특성을 분석하 고 해상풍력 경관의 유형을 분류했다. 셋째, 해상풍력 경관의 유형별로 경관 선호 매트릭스 요소와 경관 선호도를 설문조사했다. 넷째, 수집된 데이터를 활용하여 해상풍력 경관 유형별 선호 매트릭스 요소에 따른 경관 선호도 관계를 분석했다. 분석 결과 해상풍력 경관 유형이 High Continuity(HCN), High Color (HCL), Low Visual Impact(LVI), High Visibility(HVI)로 분류되었다. 또한, HCN경관 선호도에 영향을 주 는 요소로는 응집성이, HCL경관 선호도에 영향을 주는 요소로는 응집성과 신비성이, LVI경관 선호도 에 영향을 주는 요소로는 응집성과 복잡성이, HVI 경관 선호도에 영향을 주는 요소로는 응집성과 가독 성이 도출되었다. 본 연구 결과를 바탕으로 해상풍력 경관 선호도 향상을 위한 전략으로 (1)시각적 영향 을 줄일 수 있는 식재계획 및 동선설계, (2)해상풍력 터빈의 생상 대비 극대화, (3)해상풍력 터빈 설치전 경관 시뮬레이션을 제안하였다.

글로벌 OTT 서비스의 등장과 급속한 확산은 기존 영상 콘텐츠 산업의 지형을 바꾸는 역할을 하고 있다. 이러한 OTT 서비스는 지역별 장벽이 존재했던 영상 콘텐츠 시장에서 콘텐츠 유통의 패러다임이 바뀌고 있다는 것과 콘텐츠 제작 영역에도 많은 영향을 주고 있다는 것을 보여준다. 결과적으로 영상 콘텐츠 산업은 제작, 유통, 소비에 이르는 산업 구조에 급격한 변동을 겪고 있다. 글로벌 OTT 사업자가 콘텐츠 유통과 소비의 중요한 부분이 되면서 나타난 중요한 제작 영역의 변화 중 하나는 전문적인 제작 능력을 갖춘 제작사들이 두각을 나타내고 있다는 사실이다. 이 논문에서는 글로벌 OTT 서비스의 급격한 확산으로 변화하게 된 제작과 유통 시스템에 대해 분석하면서 앞으로의 산업 동향에 대해서 정리하고 있다.

국내에서 유튜브가 정식으로 서비스를 시작한 이후로 다양한 CCM 영상 콘텐츠의 제작이 늘고 있다. 이제 CCM 영상 콘텐츠의 제작은 다양한 활동을 위한 필수적인 요소로 자리 잡고 있으며 이 는 국내 CCM 문화의 성장에 좋은 양분이 되고 있다. 하지만 이와 관련된 연구가 전무한 실정이다. 지속적인 발전을 위해 관련 연구에 관한 논의가 활발히 이루어져야 함을 생각하였다. 그리고 본 연구를 통해 유튜브를 통한 CCM 영상 콘텐츠의 제작과 국내 CCM 문화에 도움이 되고자 하였다. 본 연구는 2010년대 이후 CCM 영상 콘텐츠를 분석하여 특성을 도출하였다. 국내 CCM 관련 콘텐츠 채널 중 2010년대 이후의 결성된 팀의 채널을 분류하여 다양한 기준을 통해 「J-US Ministry」와 「WELOVE CREATIVE TEAM」 그리고 「아이자야씩스티원(Isaiah6tyOne)」을 선정하였다. 그리고 각 채널의 콘텐츠와 음악을 중심으로 분석하여 도출해낸 특성은 이러하다. 첫째, 온라인 콘텐츠의 특성이 활용되었다. 시공간의 제약을 벗어나는 주제와 새로운 촬영기법을 활용한 콘텐츠가 제작 되었고, 무대 연출과 연행방식에서도 새로운 시도가 있었다. 둘째, 새로운 노랫말의 표현을 사용 하였다. ‘공감’이라는 일상적 단어와 ‘시간’과 ‘뚫다’라는 다른 개념의 단어가 혼합된 노 랫말을 사용하였다. 셋째, 기존의 형태를 벗어나는 음악적 시도가 있었다. 예배음악의 일반적인 음악적 특징에서 벗어나 5도 이상의 도약을 사용하였고, 2옥타브 솔#(G#)의 다소 높은 음의 사용 이 있었다. 본 연구는 유튜브를 통한 국내 CCM 관련 콘텐츠 중 일부만을 다루었다는 점에서 아쉬 움을 남긴다. 그리고 이와 관련된 연구가 계속해서 진행되기를 기대한다.

For highly contaminated elements such as reactor pressure vessels or reactor internals, it is a viable option to cool-down and dismantle these elements in submerged (e.g. underwater) state. Several tools and processes such as saw cutting, water jet cutting or plasma cutting are currently used for underwater cutting, with each of them having their own advantages and disadvantages. The main disadvantage of these existing methods, especially saw and water jet cutting, is the generation of secondary waste that then needs to be filtered out of the water. In addition, in the case of water jet cutting, a considerable amount of abrasive material is added, which must also be stored. To overcome this drawback, the feasibility of using laser cutting under water to minimize secondary waste production has been actively studied recently. One of the challenges with the underwater laser cutting is to visually monitor the cutting process. Flowing air bubbles generated by the cutting gas and the flashing light emitted from the laser and melting material prohibit visual monitoring of the cutting process. This study introduces a method to enhance the video from a monitoring camera. Air bubbles can be detected by computing optical flows and the video quality can be enhanced by selective removal of the detected bubbles. In addition, suppressing the frame image update from flashing light area can also effectively enhance the video quality. This paper describes the simple yet effective video quality enhancement method and reports preliminary results.

This prospective, observational study of acute stroke survivors was completed to report our clinical application of the Penetration-Aspiration Scale (PAS), an 8-point multidimensional assessment, used in conjunction with the Video Fluoroscopic Swallow Study (VFSS). In addition, we were interested in determining the association of PAS scores at admission, demographics and clinical characteristics with functional recovery (measured by the Functional Independence Measure [FIM]) at discharge from an inpatient rehabilitation hospital. There were thirty-five patients that met inclusion and consented. Out of the 35, 34 (97%) were successfully assessed with the PAS with VFSS. Multivariate regression model revealed that the PAS scores, sex, length of stay, and admission FIM scores were significantly associated with functional recovery at hospital discharge (all p values < .05). We conclude that the PAS was feasible to administer with VFSS and implement in our inpatient setting. The PAS provided information about the depth of the airway invasion, material remaining after the swallow, and the response to aspiration, which were not reported in a standardized way prior to this study. The association between the PAS and functional recovery at discharge suggests that stroke survivors with swallowing impairment have less functional improvement noted at discharge than those with better swallowing scores. Therefore, people with dysphagia may need additional services and care. Future research should determine if using the PAS can improve clinical practice and ensure consistency across care transitions (expansion), as those with dysphagia may need additional services and care.

In this study, the PBL class was applied to a Vietnamese video class. 13 learners were divided into 3 groups, and it was examined whether all learners grew toward their learning goals, and if so, through what process they grew. When group 1 announces a task, groups 2 and 3 perform peer evaluation and submit a peer evaluation sheet. For this, group 1 categorized the contents of peer evaluation into reflecting, partially reflecting, revised, and not reflecting to prepare the presentation evaluation sheet. Further, they were required to write a reason why “not reflecting.” A reflection log was also submitted. As a result of the above, the assignments were immediately revised, supplemented, and developed every week through peer and instructor evaluation. Through this process, the instructor learned in detail what kind of reflection the presenter and team members were doing each week. In particular, learners achieved ‘up-leveling’ with the activeness of immediately accepting each other’s strengths while conducting peer evaluations on each other’s presentations. This shows that the weekly assignments are improved, while the competencies of both the presentation team and peers are developed at the same time.

한류 콘텐츠의 탄생과 확산이 융성되는 현시점에서 본고는 K-dance 의 주제적 개념 정립을 토대로 ‘Feel the rhythm of Korea1’ 춤의 성 공 원인을 도출하여, K-dance의 미래 방향성을 도모하고자 했다. 그 결 과, 첫째 K-dance의 주제적 개념은 전형典型적으로 내려온 한국춤에서 부터 한국 문화 창달의 힘으로 한국인에 의해 창착된 춤까지를 아울러 가리키며, 한국 문화의 정체성 및 세계적인 비전을 통해 인류의 공감을 형성할 수 있는 콘텐츠라고 할 수 있다. 둘째, ‘Feel the rhythm of Korea1’의 춤은 이날치 수궁가에 맞춰서 춤의 원리·표현 양식·구성·의도 요소들을 기발, 코믹, 자유롭게 교차시켜서 보여준다. 한국과 세계 사이, 일상과 예술 사이, 전통과 현대 사이의 경계선 위에서 고도의 음악적 분 석과 그에 따른 동작의 수행으로 탄생한 이 춤은 한국을 제대로 알렸다. 인간의 보편적인 리듬과 움직임을 토대로 세계적인 감각, 니즈, 코드 요 소들을 가미하여 한국적인 완성체로 끌어냈다. 이는 이 춤을 K-dance 의 성공적인 사례로 꼽는 이유이기도 하다. 셋째, 안무자의 철학, 자유자 재한 표현, 음악에 대한 이해, 숙련된 동작 기술의 채용, 보편적 기호의 포착, 현대적 니즈의 반영을 토대로 그것을 조화롭게 춤으로 만들어 낼 수 있는 창작력이 K-dance 성공의 핵심 열쇠이다. 더불어 K-dance의 자유로운 도전을 장려해 주고, 창작자 육성과 지원에 주력하고, 해외 시 장 진출의 전략적인 지휘가 가능한 기획 시스템이 구축된다면, K-dance 미래는 더욱 기대해도 좋을 것이다.

Through the process of chemical vapor deposition, Tungsten Hexafluoride (WF6) is widely used by the semiconductor industry to form tungsten films. Tungsten Hexafluoride (WF6) is produced through manufacturing processes such as pulverization, wet smelting, calcination and reduction of tungsten ores. The manufacturing process of Tungsten Hexafluoride (WF6) is required thorough quality control to improve productivity. In this paper, a real-time detection system for oxidation defects that occur in the manufacturing process of Tungsten Hexafluoride (WF6) is proposed. The proposed system is implemented by applying YOLOv5 based on Convolutional Neural Network (CNN); it is expected to enable more stable management than existing management, which relies on skilled workers. The implementation method of the proposed system and the results of performance comparison are presented to prove the feasibility of the method for improving the efficiency of the WF6 manufacturing process in this paper. The proposed system applying YOLOv5s, which is the most suitable material in the actual production environment, demonstrates high accuracy (mAP@0.5 99.4 %) and real-time detection speed (FPS 46).

본고는 비대면 환경에서의 영상번역수업에 학습자중심 교육을 적용한 사례 연구이 다. 학습자가 무엇에 관심과 흥미가 있는지 개인프로필을 조사하였고, ZOOM 소회의 실 기능의 조별활동을 통해 학습자간의 상호작용을 최대한 높이고, 개별 피드백과 조별 피드백, 이클래스 LMS를 통해 학습자와 교수자와의 소통을 활성화한 수업이 되도록 진행하였다. 한 학기 동안 학습자중심으로 설계하고 운영한 영상번역수업에 대한 학습자의 강의평가와 만족도를 살펴보았다. 학습자들이 직접 번역하고 교수자 의 피드백을 받고, 조별 활동의 토론을 통해 서로의 의견을 교환한 뒤 조별 완성본 을 제출하면서 수업에 대한 성취감과 만족감이 높은 것으로 확인되었다.

Purpose: This study aimed to develop a web-based video program related to abnormal mental disorder behaviors in standardized patients and verify its effectiveness for nursing students. Methods: This study consisted of pre-test and post-test for a non-equivalent control group design. The participants were 46 nursing students(experimental group: 23, control group: 23). The experimental group was trained in a video program that applied standardized patients, while the control group received traditional training. Data collected from March to June, 2020, were analyzed using IBM SPSS Statistics for Windows, version 25.0, chi-square test, Fisher's exact test, Mann-Whitney U test, and independent t-test. Results: The difference between the experimental and control groups was statistically significant in terms of learning satisfaction (Z=2.08, p=.038), learning self-efficacy(t=2.80, p=.009), motivation for transfer(t=3.45, p=.001), and clinical reasoning competence(t=2.28, p=.028). Conclusion: This study showed that a video program on abnormal mental disorder behaviors in standardized patients is an effective tool for mental health nursing education.

As Deepfakes phenomenon is spreading worldwide mainly through videos in web platforms and it is urgent to address the issue on time. More recently, researchers have extensively discussed deepfake video datasets. However, it has been pointed out that the existing Deepfake datasets do not properly reflect the potential threat and realism due to various limitations. Although there is a need for research that establishes an agreed-upon concept for high-quality datasets or suggests evaluation criterion, there are still handful studies which examined it to-date. Therefore, this study focused on the development of the evaluation criterion for the Deepfake video dataset. In this study, the fitness of the Deepfake dataset was presented and evaluation criterions were derived through the review of previous studies. AHP structuralization and analysis were performed to advance the evaluation criterion. The results showed that Facial Expression, Validation, and Data Characteristics are important determinants of data quality. This is interpreted as a result that reflects the importance of minimizing defects and presenting results based on scientific methods when evaluating quality. This study has implications in that it suggests the fitness and evaluation criterion of the Deepfake dataset. Since the evaluation criterion presented in this study was derived based on the items considered in previous studies, it is thought that all evaluation criterions will be effective for quality improvement. It is also expected to be used as criteria for selecting an appropriate deefake dataset or as a reference for designing a Deepfake data benchmark. This study could not apply the presented evaluation criterion to existing Deepfake datasets. In future research, the proposed evaluation criterion will be applied to existing datasets to evaluate the strengths and weaknesses of each dataset, and to consider what implications there will be when used in Deepfake research.