• Electronics and Telecommunications Trends
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• v.37 no.4
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• pp.70-80
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• 2022

With the rapid growth in media service platforms providing broadcast programs or content services, content production has become more important and competitive. As a strategy to meet the diverse needs of global consumers for a variety of content and to retain them as long-term repeat customers, global over-the-top service providers are increasing not only the number of content productions but also their production efficiency. Moreover, a considerable amount of scene composition in the flow of content production work appears to be combined with rendering technology from a game engine and converted to object-based computer programming, thereby enhancing the creativity, diversity, quality, and efficiency of content production. This study examines the latest technology trends in content production such as virtual studio technology, which has emerged as the center of content production, the use cases in various fields of artificial intelligence, and the metadata standards for content search or scene composition. This study also examines the possibility of using object-based computer programming as one of the future candidate technologies for content production.

• International journal of advanced smart convergence
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• v.11 no.3
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• pp.132-141
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• 2022

We look at the rocket life stages of NaverZ's metaverse platform scaling and investigate the ignition and scale-up stage of its metaverse platform brand, Zepeto based on the Rocket Model (RM). The results are derived as follows: Firstly, NaverZ shows the event strategy by collaborating with K-pops, the piggybacking strategy by utilizing other SNSs, and the VIP strategy by investing in game and entertainment content genres in the 'attract' function. In the second 'match' function, based on the matching rule of Zepeto, the users can generate their own characters and "World" with Zepeto Studio. However, for strengthening the matching quality, NaverZ is investing in the artificial intelligence (AI) based companies consistently. In the 'connect' function, NaverZ's maximization of the positive interaction is possible by inducing feed activities in Zepeto & other SNSs and by uploading attractive content for viral effects in the ignition. For facilitating this, NaverZ expands the scale to other continents like Southeast Asia and Middle East with the localization strategy inclusive investment. Lastly, in the 'transact' function, based on three monetization experiments like Coin & ZEM, user generated content (UGC) fee, and advertising revenue in the ignition, NaverZ starts to invest in NFT platforms and abroad blockchain companies.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.569-572
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• 2023

최근 하루가 다르게 발전하고 있는 생성형 AI가 창작과 예술 분야에 어떤 영향을 미칠 수 있는지, 새롭게 등장하고 있는 다양한 분야에서 활용 가능한 획기적인 기능 등을 살펴보고 이를 바탕으로 새로운 창작 방향을 제시할 수 있는 방법들을 살펴보려 한다. 최근, 작곡가와 소설가들은 물론, 디지털 아티스트들까지도 생성형 AI를 활용하여 독특한 음악, 글, 그리고 이미지를 창조하는데 성공했다는 사례들이 속속 드러나고 있고 영상, 게임, 웹툰 등 많은 산업현장에서 직접적인 활용방법에 대한 연구결과가 등장하고 실제 적용 사례도 늘어나고 있다. 이미지 생성기인 미드저니와 스테이블디퓨전 같은 도구들은 혁신적인 방법으로 빠르게 높은 퀄리티의 이미지를 생성하고 다양한 아이디어를 제공 받을 수 있는 도구로 창작과 예술 분야에서 큰 관심을 받고 있다. 이러한 발전은 창작과 예술 분야에서 생성형 AI의 무한한 가능성을 보여주는 한편, 인간의 창의성 침해와 예술가들의 노력 희석에 대한 비판적 시각을 불러일으키기도 한다. 본 연구는 이런 다양한 관점에서 창작·예술 분야의 생성형 AI 활용을 깊이 있게 탐구한다. 그 과정에서 여러 생성형 AI 도구들, 특히 이미지 생성기 미드저니와 스테이블디퓨전의 기능과 활용 방안, 그로 인한 사회적, 윤리적 측면을 분석하며, 창작·예술 분야에서의 생성형 AI 활용의 적절한 방향성과 미래 전망을 제시해 보고자 한다.

• Journal of Korea Game Society
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• v.9 no.3
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• pp.139-150
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• 2009

This paper attempts to investigate the establishment of an interactive knowledge base for action planning by virtual agents and an interactive knowledge-based planning system. A fixed knowledge base is unable to properly handle a change in circumstances because fixed planning is only available under a fixed knowledge base. Therefore, this paper proposes the establishment of an interactive knowledge base which is applicable to diverse environments and an artificial intelligence planning system in which an interactive knowledge base is available. The interactive knowledge base proposed in this paper consists of motivation, behavior, object and action. The association relationship between knowledge base and its input is set using an automation tool. With this tool, a user can easily add to or amend the components of the knowledge base. With this knowledge base, a character plans all action items and chooses one of them to take an action. Since a new action can be applicable by updating the knowledge base even when the character environment changes, it is very useful for virtual reality content developers. This paper has established a relationship between scalable interactive knowledge base components and other components and proposes a convenient input tool and a planning system algorithm effective for an interactive knowledge base. The results of this study have been verified through testing in a virtual environment ('virtual library').

• The Journal of Bigdata
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• v.5 no.2
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• pp.77-84
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• 2020

In this study, we conducted a study on the win-loss predicton analysis of korean professional baseball by artificial intelligence models. Based on the model, we predicted the winner as well as each team's final rank in the league. Additionally, we developed a website for viewers' understanding. In each game's first, third, and fifth inning, we analyze to select the best model that performs the highest accuracy and minimizes errors. Based on the result, we generate the rankings. We used the predicted data started from May 5, the season's opening day, to August 30, 2020 to generate the rankings. In the games which Kia Tigers did not play, however, we used actual games' results in the data. KNN and AdaBoost selected the most optimized machine learning model. As a result, we observe a decreasing trend of the predicted results' ranking error as the season progresses. The deep learning model recorded 89% of the model accuracy. It provides the same result of decreasing ranking error trends of the predicted results that we observe in the machine learning model. We estimate that this study's result applies to future KBO predictions as well as other fields. We expect broadcasting enhancements by posting the predicted winning percentage per inning which is generated by AI algorism. We expect this will bring new interest to the KBO fans. Furthermore, the prediction generated at each inning would provide insights to teams so that they can analyze data and come up with successful strategies.

• Journal of the Korean Dietetic Association
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• v.28 no.4
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• pp.281-292
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• 2022

The lower elementary school grades are an important period in which eating habits are formed. We examined the relationship between eating habits and school meal intake in the lower grades of an elementary school in Seoul. The eating habits were investigated using the Nutrition Quotient (NQ) for children. The school meal intake rates and preferred menus were obtained by automatically scanning the plate before and after meals using an artificial intelligence food scanner. The average school meal intake rate for the 347 subjects was 68.5±12.2%, and the nutrient intakes through the school meals were 353.5±70.0 kcal of energy, 51.8±10.2 g of carbohydrates, 14.6±3.1 g of proteins, 10.3±2.3 g of fats, 87.0±20.0 mg of calcium, and 1.8±0.4 mg of iron. The preferred menus were rice, grilled food, and dairy products, and non-preferred menus were salad, beverages, and stewed food. The eating habits that showed a positive correlation with the school meal intake rate were 'Diverse side dishes (r=0.332, P<0.001)', 'Vegetable side dishes (r=0.166, P<0.01)', 'Kimchi side dish (r=0.230, P<0.001)' and 'Less TV watching and computer game time (r=0.105, P<0.05)'. The NQ score also showed a positive correlation with the rate of school meal intake (r=0.216, P<0.001). The balance score was positively correlated with fruit (r=0.192, P<0.001), and the diversity score had the highest positive correlation with Kimchi (r=0.362, P<0.001). The regularity score was positively correlated with fried food (r=0.114, P<0.05). In conclusion, it was found that elementary school students in the lower grades had a higher school meal intake rate when their eating habits included eating side dishes evenly, and consuming vegetable side dishes and Kimchi.

• Journal of Korea Multimedia Society
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• v.12 no.5
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• pp.745-756
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• 2009

An autonomous virtual character can conduct itself like a human after recognizing and interpreting the virtual environment. Artificial vision is mainly used in the recognition of the environment for a virtual character. The present artificial vision that has been developed takes all the information at once from everything that comes into view. However, this can reduce the efficiency and reality of the system by saving too much information at once, and it also causes problems because the speed slows down in the dynamic environment of the game. Therefore, to construct a vision system similar to that of humans, a visual observation system which saves only the required information is needed. For that reason, this research focuses on the descriptive artificial intelligence engine which detects the most important information visually recognized by the character in the virtual world and saves it into the memory by degrees. In addition, a visual system is constructed in accordance with an image transaction theory to make it sense and recognize human feelings. This system finds the attention area of moving objects quickly and effectively through the experiment of the virtual environment with three dynamic dimensions. Also the experiment enhanced processing speed more than 1.6 times.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.3
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• pp.25-30
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• 2009

Recent days intellectual systems using reinforcement learning are being researched at various fields of game and web searching applications. A good training models are called to be fitted with trainning data and also classified with new records accurately. A overfitted model with training data may possibly bring the unfavored fallacy of hasty generalization. But it would be unavoidable in actual world. The entropy and mutation model are suggested to reduce the overfitting problems on this paper. It explains variation of entropy and artificial development of entropy in datamining, which can tell development of mutation to survive in nature world. Periodical generation of maximum entropy are introduced in this paper to reduce overfitting. Maximum entropy model can be considered as a periodical generalization in intensified process of intellectual web searching.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.4
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• pp.717-724
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• 2020

Due to the important role played by emotion in human interaction, affective computing is dedicated in trying to understand and regulate emotion through human-aware artificial intelligence. By understanding, emotion mental diseases such as depression, autism, attention deficit hyperactivity disorder, and game addiction will be better managed as they are all associated with emotion. Various studies for emotion recognition have been conducted to solve these problems. In applying machine learning for the emotion recognition, the efforts to reduce the complexity of the algorithm and improve the accuracy are required. In this paper, we investigate emotion Electroencephalogram (EEG) feature reduction and classification using Stack AutoEncoder (SAE) and Long-Short-Term-Memory/Recurrent Neural Networks (LSTM/RNN) classification respectively. The proposed method reduced the complexity of the model and significantly enhance the performance of the classifiers.

• Smart Structures and Systems
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• v.29 no.1
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• pp.251-266
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• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.