• 공학교육연구
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• 제24권6호
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• pp.79-85
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• 2021

In a future society where cutting-edge science technology such as artificial intelligence becomes commonplace, the demand for talented people with basic knowledge of mathematics and science is expected to increase continuously, and the educational infrastructure suitable for the characteristics of future generations is still insufficient. In particular, in the case of students taking convergence courses including practical training, there was a problem in communication with the instructor. In this study, we looked at the current status of distance learning at domestic universities that came suddenly due to the global pandemic of COVID-19. In addition, a case study of the use of technology was conducted to facilitate the interaction between instructors and learners through case analysis of distance classes in convergence subjects. Therefore, this study aims to introduce the case of developing lecture contents for smooth convergence education in a non-face-to-face educational environment targeting the developed AI convergence courses and applying them to the education of enrolled students.

• 전자통신동향분석
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• 제34권6호
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• pp.100-107
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• 2019

Recently, reinforcement learning (RL) has expanded from the research phase of the virtual simulation environment to a wide range of applications, such as autonomous driving, natural language processing, recommendation systems, and disease diagnosis. However, RL is less likely to be used in these complex real-world environments. In contrast, inverse reinforcement learning (IRL) can obtain optimal policies in various situations; furthermore, it can use expert demonstration data to achieve its target task. In particular, IRL is expected to be a key technology for artificial general intelligence research that can successfully perform human intellectual tasks. In this report, we briefly summarize various IRL techniques and research directions.

• International Journal of Computer Science & Network Security
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• 제22권11호
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• pp.229-235
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• 2022

This study aims to substantiate an interdisciplinary methodology for automated analysis and qualitative assessment of legislation and court decisions. The development of this kind of methodology will make it possible to fill a number of methodological gaps in various research areas, including law effectiveness assessment and legal monitoring. We have defined a methodology based on the interdisciplinary principles and tools. In general, it should be noted that even at the level of qualitative assessment made with the use of the methodology described above, the accumulation of knowledge about the relationship between legal objectives, indicators and computer methods of their identification can reduce the role of expert knowledge and subjective factor in the process of assessment, planning, forecasting and control over the state of legislation and law enforcement. Automation of intellectual processes becomes inevitable in a digital society, but, releasing experts from routine work, simultaneously reorients it to development of interdisciplinary methods and control over their application.

• International journal of advanced smart convergence
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• 제12권3호
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• pp.186-191
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• 2023

This study would investigate the generative AIs currently in service in the era of hyperscale AIs and explore measures for the use of generative AIs, focusing on 'ChatGPT,' which has received attention as a leader of generative AIs. Among the various generative AIs, this study selected ChatGPT, which has rich application cases to conduct research, investigation, and use. This study investigated the concept, learning principle, and features of ChatGPT, identified the algorithm of conversational AI as one of the specific cases and checked how it is used. In addition, by comparing various cases of the application of conversational AIs such as Google's Bard and MS's NewBing, this study sought efficient ways to utilize them through the collected cases and conducted research on the limitations of conversational AI and precautions for its use. If connected to city-related databases, it can provide information on city infrastructure, transportation systems, and public services, so residents can easily get the information they need. We want to apply this research to enrich the lives of our citizens.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 춘계학술대회
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• pp.47-49
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• 2022

Robot navigation has seen a major improvement since the the rediscovery of the potential of Artificial Intelligence (AI) and the attention it has garnered in research circles. A notable achievement in the area was Deep Learning (DL) application in computer vision with outstanding daily life applications such as face-recognition, object detection, and more. However, robotics in general still depend on human inputs in certain areas such as localization, navigation, etc. In this paper, we propose a study case of robot navigation based on deep reinforcement technology. We look into the benefits of switching from traditional ROS-based navigation algorithms towards machine learning approaches and methods. We describe the state-of-the-art technology by introducing the concepts of Reinforcement Learning (RL), Deep Learning (DL) and DRL before before focusing on visual navigation based on DRL. The case study preludes further real life deployment in which mobile navigational agent learns to navigate unbeknownst areas.

• International Journal of Internet, Broadcasting and Communication
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• 제15권4호
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• pp.233-238
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• 2023

In the Fourth Industrial Revolution era, creative ideas are creating enormous value. This study conducted a case study on curriculum management plans aimed at protecting ideas and their results, recognizing the importance of intellectual property (IP), and cultivating basic knowledge about intellectual property. In particular, this study looked at ways to quickly learn related issues regarding new intellectual property rights related to computer software and artificial intelligence. In addition, research was conducted on ways to learn about efficient protection and utilization of inventions through actual examples. This study checked the importance and necessity of the interaction and communication between instructors and learners through the status of distance learning in domestic universities and a case study of distance learning of convergence subjects. We aim to continuously research effective class management methods and contribute to academic development through case studies of convergence subjects.

• 대한소아치과학회지
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• 제51권1호
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• pp.1-10
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• 2024

Deep learning algorithms are becoming more prevalent in dental research because they are utilized in everyday activities. However, dental researchers and clinicians find it challenging to interpret deep learning studies. This review aimed to provide an overview of the general concept of deep learning and current deep learning research in dental radiographic image analysis. In addition, the process of implementing deep learning research is described. Deep-learning-based algorithmic models perform well in classification, object detection, and segmentation tasks, making it possible to automatically diagnose oral lesions and anatomical structures. The deep learning model can enhance the decision-making process for researchers and clinicians. This review may be useful to dental researchers who are currently evaluating and assessing deep learning studies in the field of dentistry.

• International Journal of Advanced Culture Technology
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• 제12권1호
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• pp.234-241
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• 2024

In higher education, integration of technologies - particularly generative artificial intelligence (AI) such as ChatGPT - has become increasingly widespread, serving numerous purposes to its stakeholders. While users acknowledge the utility of technology, concerns have emerged regarding its misuses. The present study is designed to investigate authentic perspectives and opinions of college freshman students to critically address the relevant concerns, and suggest meaningful solutions. To this end, seven college freshman student participants were recruited in a four-days-long online questionnaire. Their responses indicated that the college student participants appear to find ChatGPT positive in terms of its practicality and usefulness. However, they also showed concerns about a few potential issues (i.e., possible plagiarism and copyright problems). With recommendations the student participants suggested to reduce the aforementioned problems, the article discusses implications of the findings, providing valuable insights into the balance between implementation of AI technologies and dealing with the associated challenges in higher education in general.

• Clinical Endoscopy
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• 제57권5호
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• pp.559-570
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• 2024

The literature pertaining to surveillance following treatment for esophageal squamous cell carcinoma (SCC) was reviewed and summarized, encompassing the current status and future perspectives. Analysis of the standardized mortality and incidence ratios for these cancers indicates an elevated risk of cancer in the oral cavity, pharynx, larynx, and lungs among patients with esophageal SCC compared to the general population. To enhance the efficacy of surveillance for these metachronous cancers, risk stratification is needed. Various factors, including multiple Lugol-voiding lesions, multiple foci of dilated vascular areas, young age, and high mean corpuscular volume, have been identified as predictors of metachronous SCCs. Current practice involves stratifying the risk of metachronous esophageal and head/neck SCCs based on the presence of multiple Lugol-voiding lesions. Endoscopic surveillance, scheduled 6-12 months post-endoscopic resection, has demonstrated effectiveness, with over 90% of metachronous esophageal SCCs treatable through minimally invasive modalities. Narrow-band imaging emerges as the preferred surveillance method for esophageal and head/neck SCC based on comparative studies of various imaging techniques. Innovative approaches, such as artificial intelligence-assisted detection systems and radiofrequency ablation of high-risk background mucosa, may improve outcomes in patients following endoscopic resection.

• 한국전자거래학회지
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• 제27권2호
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• pp.175-192
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• 2022

자연어 처리 기술을 접목한 컴퓨터 보조 언어 학습 연구가 진행되고 있지만, 기존 영문교정은 일반적인 영어 문장을 기반으로 연구되어, 격식을 갖춘 문체와 전문적인 기술 용어를 사용하는 학술 영문의 경우 그 특성을 반영하지 못한 교정 결과를 제공한다. 또한 문장의 문법적 완성도 향상을 위한 다수의 기존 연구는 교정을 통한 문장 전달력 향상의 한계점이 존재한다. 따라서, 본 논문은 전문적인 기술 용어 사용을 기반으로 문장의 명확한 의미 전달을 목적으로 하는 학술 영문을 위한 자동 교정 방법론을 제안한다. 제안 방법론은 오탈자 교정과 문장 전달력 개선 두 단계로 구성된다. 오탈자 교정 단계는 입력된 오탈자와 문맥에 적합한 교정 단어를 제공한다. 문장 전달력 개선 단계는 원문과 교정문의 쌍으로부터 학습할 수 있는 양방향 순환신경망 기계번역 사후교정 모델을 기반으로 문장의 전달력을 개선한다. 실제 교정 데이터를 이용한 실험을 수행하였으며, 정량적·정성적 분석을 통해 제안 방법론의 우수성을 검증하였다.