• Trans-
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• v.9
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• pp.67-92
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• 2020

The development of social platforms and digital technology has promoted the age of the communication in our society. As online communication has become commonplace, expressing feelings, thoughts and experiences on the Internet has become an everyday routine. Among them, SNS is one of the representative platforms for expressing oneself easily and interacting with other users. The way of communicating with the SNS about what they did and what experiences they experienced from one's everyday lives became more common. As a result, the museum makes various efforts to enhance visitors' attention and interest with the use of SNS. It provides a content-based programs and museum environment that allow visitors to enjoy playing and learning at the same time. This study will explore not only a simple appreciation, but also the way of communicating to everyday life in terms of the changes for museum environment through the development, implementation and adaptations of digital technology. Through this, mobile-based communication with SNS provides various values and quality of museum visit, can be completed with meaningful museum experience, and various roles and functions of the museum are examined in terms of social platform of experience.

• Journal of Digital Convergence
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• v.19 no.12
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• pp.51-62
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• 2021

This study developed the factors capable of systematic/comprehensive evaluation of the task performance in order to strengthen the performance management of the professional career personnel invitation program (PCPIP). To this end, a performance evaluation framework was developed by analyzing existing project evaluation studies based on boundary theory and Kirkpatrick's four-level evaluation model. Afterwords, through two Delphi surveys, evaluation factors that can measure performance in terms of individual and invitation institutions of PCP were derived and validated. With this procedure, five evaluation factors were finally selected: adaptability, connectivity, clarity, compatibility, and expandability. This study has implications suggesting a performance evaluation factors capable of hybrid quantitative/qualitative evaluation for the performance management of PCPIP operated by National Research Foundation of Korea Research since 1994.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.269-275
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• 2022

As artificial intelligence services targeting humans increase, social demands are increasing that artificial intelligence should also be made on an ethical basis. Following this trend, the government and businesses are preparing policies and norms related to artificial intelligence ethics. In order to establish reasonable policies and norms, the first step is to understand the public's perceptions. In this paper, social data and news comments were collected and analyzed to understand the public's perception related to artificial intelligence and ethics. Interest analysis, emotional analysis, and discourse analysis were performed and visualized on the collected datasets. As a result of the analysis, interest in "artificial intelligence ethics" and "artificial intelligence" favorability showed an inversely proportional correlation. As a result of discourse analysis, the biggest issue was "personal information leakage," and it also showed a discourse on contamination and deflection of learning data and whether computer-made artificial intelligence should be given a legal personality. This study can be used as data to grasp the public's perception when preparing artificial intelligence ethical norms and policies.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.565-567
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• 2022

질병을 앓고 있는 환자는 상태에 따라 병실, 주거지, 요양원 등 조호환경 내 생활 시 의료 인력의 지속적인 추적 및 관찰을 통해 신체에 이상이 생긴 경우 이를 감지하고, 신속하게 조치할 수 있도록 해야 한다. 의료 인력이 직접 환자를 확인하는 방법은 의료 인력의 반복적인 노동이 요구되며 실시간으로 환자를 확인해야 한다는 특성상 의료 인력이 상주해야 하기에 이는 곧, 의료 인력의 부족과 낭비로 이어진다. 해당 문제 해결을 위해 의료 인력을 대신하여 조호환경 내 환자의 상태를 실시간으로 모니터링할 수 있는 딥러닝 모델들이 연구되고 있다. 딥러닝 모델은 데이터의 수가 많을수록 강인한 모델을 설계할 수 있으며, 데이터셋의 배경, 객체의 특징 분포 등 다양한 조건에 영향을 받기 때문에 학습에 필요한 도메인을 가지는 많은 양의 전처리된 데이터를 수집해야 한다. 따라서, 조호환경 내 환자에 대한 데이터셋이 필요하지만, 공개된 데이터셋의 경우 양이 매우 적으며 이를 반전, 회전기법 등을이용할 경우 데이터의 수를 늘릴 수 있지만, 같은 분포의 특징을 가지는 데이터가 생성되기에 데이터 증강 기법을 단순하게 적용하면 딥러닝 모델의 과적합을 야기한다. 또한, 조호환경 내 이미지 데이터셋은 얼굴 노출과 같은 개인정보가 포함 될 수 있으며 이를 보호하기 위해 정보들을 비식별화 해야 한다는 문제점이 있다. 따라서 본 논문에서는 조호환경에서 수집된 데이터 증강을 위한 Style-Generative Adversarial Networks 기법을 적용하여 조호환경 데이터셋 수집에 효과적인 증강 기법을 제안한다.

• Journal of Industrial Convergence
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• v.21 no.8
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• pp.87-96
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• 2023

The purpose of this study was to investigate the meanings that pre-service secondary physical education teachers attach to learning-centered teaching through metaphor analysis and to examine their perceptions of the application of learning-centered teaching in physical education. In order to achieve the purpose of the study, a survey was conducted to express the perception of learning-centered teaching in metaphors. The data analysis was processed through the process of coding, metaphor classification, determining the unit of analysis, and categorisation of the sample according to the metaphor analysis method. As a result, four major categories of educational needs, educational goals and contents, teaching and learning methods and evaluation, teacher education and roles, and eight content types were derived: lessons for self-actualisation, lessons for preparing for future society, lessons that foster creativity, open and diverse lessons, lessons that stimulate curiosity and interest, lessons that are suitable for individual development and characteristics, lessons that are burdensome to implement, and lessons that require teacher creativity and originality. The results of this study provide implications for the necessity and direction of learning-centered class.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.249-263
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• 2023

Collaborative Filtering, a representative recommendation system methodology, consists of two approaches: neighbor methods and latent factor models. Among these, the latent factor model using matrix factorization decomposes the user-item interaction matrix into two lower-dimensional rectangular matrices, predicting the item's rating through the product of these matrices. Due to the factor vectors inferred from rating patterns capturing user and item characteristics, this method is superior in scalability, accuracy, and flexibility compared to neighbor-based methods. However, it has a fundamental drawback: the need to reflect the diversity of preferences of different individuals for items with no ratings. This limitation leads to repetitive and inaccurate recommendations. The Adaptive Deep Latent Factor Model (ADLFM) was developed to address this issue. This model adaptively learns the preferences for each item by using the item description, which provides a detailed summary and explanation of the item. ADLFM takes in item description as input, calculates latent vectors of the user and item, and presents a method that can reflect personal diversity using an attention score. However, due to the requirement of a dataset that includes item descriptions, the domain that can apply ADLFM is limited, resulting in generalization limitations. This study proposes a Generalized Adaptive Deep Latent Factor Recommendation Model, G-ADLFRM, to improve the limitations of ADLFM. Firstly, we use item ID, commonly used in recommendation systems, as input instead of the item description. Additionally, we apply improved deep learning model structures such as Self-Attention, Multi-head Attention, and Multi-Conv1D. We conducted experiments on various datasets with input and model structure changes. The results showed that when only the input was changed, MAE increased slightly compared to ADLFM due to accompanying information loss, resulting in decreased recommendation performance. However, the average learning speed per epoch significantly improved as the amount of information to be processed decreased. When both the input and the model structure were changed, the best-performing Multi-Conv1d structure showed similar performance to ADLFM, sufficiently counteracting the information loss caused by the input change. We conclude that G-ADLFRM is a new, lightweight, and generalizable model that maintains the performance of the existing ADLFM while enabling fast learning and inference.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.217-242
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• 2022

With the launch of Artificial Intelligence(AI)-based intelligent products on the market, innovative changes are taking place not only in business but also in consumers' daily lives. Intelligent products have the potential to realize technology differentiation and increase market competitiveness through advanced functions of artificial intelligence. However, there is no new product development methodology that can sufficiently reflect the characteristics of artificial intelligence for the purpose of developing intelligent products with high market acceptance. This study proposes a KANO-QFD integrated model as a methodology for intelligent product development. As a specific example of the empirical analysis, the types of consumer requirements for hair loss prediction and treatment device were classified, and the relative importance and priority of engineering characteristics were derived to suggest the direction of intelligent medical product development. As a result of a survey of 130 consumers, accurate prediction of future hair loss progress, future hair loss and improved future after treatment realized and viewed on a smartphone, sophisticated design, and treatment using laser and LED combined light energy were realized as attractive quality factors among the KANO categories. As a result of the analysis based on House of Quality of QFD, learning data for hair loss diagnosis and prediction, micro camera resolution for scalp scan, hair loss type classification model, customized personal account management, and hair loss progress diagnosis model were derived. This study is significant in that it presented directions for the development of artificial intelligence-based intelligent medical product that were not previously preceded.

• Journal of Korean Elementary Science Education
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• v.43 no.2
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• pp.337-352
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• 2024

This study develops an informal science education program based on a creative design for the promotion of participation and action and explores the validity and effectiveness of the program. The program consists of three mini-projects, namely, The Martian: Surviving from the Mars, So Good to Have Seonjeongneung, and Designing a Sunshine Restaurant, with distinct topics focusing on the localization and contextualization of the region in which the program was implemented to encourage students' participation and action. The researchers selected three topics for each mini-project through discussions and revisions. The program is designed for 42 lessons across the three mini-projects and offers a presentation session in which students present their learnings from the program, constructed to facilitate personal contextualization. Participants included 15 elementary school students from Grades 5 and 6 who took surveys before and after the program. The pre-post surveys comprised two dimensions, 'Participation and Action' and 'Positive Experience about Science,' to examine the effectiveness of the program. At the end of the survey, we added a self-descriptive question that allows students to freely write down their feelings and thoughts. We checked the students' thoughts. The effectiveness of the program was illustrated by the largest increase in the "safe society" category, whereas there was little increase in other categories under the 'participation and Action' dimension in the Korean Science Education Standards. 'Science-related Self-concept' exhibited the largest increase, whereas the other categories displayed little increase from the 'Positive Experience about Science' dimension category.

• Journal of the Korea Convergence Society
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• v.9 no.1
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• pp.419-431
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• 2018

In this paper, we propose a novel method for monitoring mood trend of Twitter users by analyzing their daily tweets for a long period. Then, to more accurately understand their tweets, we analyze all types of content in tweets, i.e., texts and emoticons, and images, thus develop a multimodal sentiment analysis method. In the proposed method, two single-modal analyses first are performed to extract the users' moods hidden in texts and images: a lexicon-based and learning-based text classifier and a learning-based image classifier. Thereafter, the extracted moods from the respective analyses are combined into a tweet mood and aggregated a daily mood. As a result, the proposed method generates a user daily mood flow graph, which allows us for monitoring the mood trend of users more intuitively. For evaluation, we perform two sets of experiment. First, we collect the data sets of 40,447 data. We evaluate our method via comparing the state-of-the-art techniques. In our experiments, we demonstrate that the proposed multimodal analysis method outperforms other baselines and our own methods using text-based tweets or images only. Furthermore, to evaluate the potential of the proposed method in monitoring users' mood trend, we tested the proposed method with 40 depressive users and 40 normal users. It proves that the proposed method can be effectively used in finding depressed users.

• Journal of dental hygiene science
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• v.4 no.2
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• pp.61-73
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• 2004

In this study, I have analyzed domestic dental clinics which had introduced CRM for customer satisfaction management. The purpose of this study is to provide information for designing efficient CRM. I would suggest some strategies to carry out CRM as followed. First, promote long-term relationship with customers. Second, select a target patient group and classify customers. Third, set a systematic database up. Fourth, make various channels to communicate with customers. In addition, the following ways are essential to be successful in implementing CRM. First, strengthen the service provided at the Moment of Truth (MOT). Second, organize learning in hospital and all the staff should be customer-oriented. Third, perform systematic "internal marketing".