• Journal of The Korean Association of Information Education
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• v.25 no.2
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• pp.289-300
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• 2021

SNS data was collected and analyzed by topic modeling techniques to examine recent trends in information education. By deriving keywords and topics for SW education and AI education, we not only attempted to discover insights ahead of the next revised curriculum but also suggested directions. According to the SNS data analysis, the contents of human resource development for software and the instructional method in schools are indicated as a high requirement. Meanwhile, SW education should be conducted through a separate curriculum from elementary school, and this was consistent with the opinion that it is necessary to be organized as a required subject. There was an opinion to support the schools since AI education is newly introduced in next revised national curriculum. The trends in SW education and AI education which are observed through SNS data analysis could be concluded to conduct the substantial operation of information education and curriculum organization.

• Korean Journal of Radiology
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• v.24 no.9
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• pp.890-902
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• 2023

Objective: The clinical impact of artificial intelligence-based computer-aided detection (AI-CAD) beyond diagnostic accuracy remains uncertain. We aimed to investigate the influence of the clinical implementation of AI-CAD for chest radiograph (CR) interpretation in daily practice on the rate of referral for chest computed tomography (CT). Materials and Methods: AI-CAD was implemented in clinical practice at the Seoul National University Hospital. CRs obtained from patients who visited the pulmonology outpatient clinics before (January-December 2019) and after (January-December 2020) implementation were included in this study. After implementation, the referring pulmonologist requested CRs with or without AI-CAD analysis. We conducted multivariable logistic regression analyses to evaluate the associations between using AI-CAD and the following study outcomes: the rate of chest CT referral, defined as request and actual acquisition of chest CT within 30 days after CR acquisition, and the CT referral rates separately for subsequent positive and negative CT results. Multivariable analyses included various covariates such as patient age and sex, time of CR acquisition (before versus after AI-CAD implementation), referring pulmonologist, nature of the CR examination (baseline versus follow-up examination), and radiology reports presence at the time of the pulmonology visit. Results: A total of 28546 CRs from 14565 patients (mean age: 67 years; 7130 males) and 25888 CRs from 12929 patients (mean age: 67 years; 6435 males) before and after AI-CAD implementation were included. The use of AI-CAD was independently associated with increased chest CT referrals (odds ratio [OR], 1.33; P = 0.008) and referrals with subsequent negative chest CT results (OR, 1.46; P = 0.005). Meanwhile, referrals with positive chest CT results were not significantly associated with AI-CAD use (OR, 1.08; P = 0.647). Conclusion: The use of AI-CAD for CR interpretation in pulmonology outpatients was independently associated with an increased frequency of overall referrals for chest CT scans and referrals with subsequent negative results.

• Journal of information and communication convergence engineering
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• v.20 no.3
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• pp.166-173
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• 2022

Recently, studies have been conducted on intelligent transportation systems (ITS) that provide safety and convenience to humans. Systems that compose the ITS adopt architectures that applied the cloud computing which consists of a high-performance general-purpose processor or graphics processing unit. However, an architecture that only used the cloud computing requires a high network bandwidth and consumes much power. Therefore, applying edge computing to ITS is essential for solving these problems. In this paper, we propose an edge artificial intelligence (AI) device based ITS. Edge AI which is applicable to various systems in ITS has been applied to license plate recognition. We implemented edge AI on a field-programmable gate array (FPGA). The accuracy of the edge AI for license plate recognition was 0.94. Finally, we synthesized the edge AI logic with Magnachip/Hynix 180nm CMOS technology and the power consumption measured using the Synopsys's design compiler tool was 482.583mW.

• Korean Journal of Artificial Intelligence
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• v.11 no.2
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• pp.7-17
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• 2023

Artificial Intelligence (AI) technology has already penetrated deeply into our daily lives, and we live with the convenience of it anytime, anywhere, and sometimes even without us noticing it. However, because AI is imitative intelligence based on human Intelligence, it inevitably has both good and evil sides of humans, which is why ethical principles are essential. The starting point of this study is the AI principles for companies or organizations to develop products. Since the late 2010s, studies on ethics and principles of AI have been actively published. This study focused on AI principles declared by global companies currently developing various products through AI technology. So, we surveyed the AI principles of the Global 500 companies by market capitalization at a given specific time and collected the AI principles explicitly declared by 46 of them. AI analysis technology primarily analyzed this text data, especially LDA (Latent Dirichlet Allocation) topic modeling, which belongs to Machine Learning (ML) analysis technology. Then, we conducted a Delphi technique to reach a meaningful consensus by presenting the primary analysis results. We expect to provide meaningful guidelines in AI-related government policy establishment, corporate ethics declarations, and academic research, where debates on AI ethics and principles often occur recently based on the results of our study.

• Structural Engineering and Mechanics
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• v.91 no.4
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• pp.393-401
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• 2024

This study explores the integration of the generative AI, specifically ChatGPT (GPT-4o), into the field of structural analysis and design using the finite element method (FEM). The research is conducted in two main parts: structural analysis and structural design. For structural analysis, two scenarios are examined: one where the FEM source code is provided to ChatGPT and one where it is not. The AI's ability to understand, process, and accurately perform finite element analysis in both scenarios is evaluated. Additionally, the application of ChatGPT in structural design is investigated, including design modifications and parameter sensitivity analysis. The results demonstrate the potential of the generative AI to assist in complex engineering tasks, suggesting a future where AI significantly enhances efficiency and innovation in structural engineering. However, the study also highlights the importance of ensuring the accuracy and reliability of AI-generated results, particularly in safety-critical applications.

• Journal of Digital Convergence
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• v.19 no.1
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• pp.141-149
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• 2021

Due to the rapid rise of artificial intelligence technology around the world, SW education conducted in elementary and secondary schools is expanding including AI education. Therefore, this study aims to present an AI education system based on 4P(Play, Problem Solving, Product Making, Project) that can be applied from kindergarten to high school. The AI education system presented in this study is designed to be applied in 4P-based Play, Problem Solving, Product Making, and Project 4 stages so that it can be applied by school age and step by step. The level was presented by dividing it into two areas: AI literacy and AI development. In order to verify the validity of the developed AI education system, the Delphi method was applied to 15 experts who had experience in SW education or AI education. The AI education system derived as a result of the verification will be able to contribute to the development of a content system for AI education at each school level in the future.

• Annual Conference on Human and Language Technology
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• 1997.10a
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• pp.403-408
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• 1997

언어의 내적 특성을 반영하는 의미 문체의 검사 및 교정은 언어의 형태적인 면과 관련있는 단순한 철자 검사 및 교정에 비해 더 난해하고 복잡한 양상을 띤다. 본 논문이 제안하는 의미 정보를 이용한 명사 분류 방법은 의미와 문체 오류의 포착과 수정 기능을 향상시키기 위한 방법의 하나이다. 이 논문은 문맥상 용법이 어긋나는 서술어를 교정하기 위해 명사 의미 분류방법을 서술어/논항의 통사 의미적 관계 분석에 이용하여 의미 규칙을 세우는 과정을 서술한다. 여기서 논항인 명사의 의미 정보를 체계적으로 분류하기 위해 시소러스 기법과 의미망을 응용한다. 서술어와 논항 사이의 통사 의미적 관계에 따라 의미 문체 오류를 검사하고 교정함으로써 규칙들을 일반화하여 구축하게 하고 이미 존재하고 있는 규칙을 단순화함으로써 한국어 문법 검사기의 기능을 보완한다.

• Journal of Microbiology and Biotechnology
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• v.13 no.5
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• pp.767-772
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• 2003

Three plasmids derived from the ${\beta}-agarase$ gene (PjaA) of Pseudomonas sp. W7 were expressed in Escherichia coli AD494(DE3) pLysS with lactose as an inducer. These products corresponded to the complete (PjaA) and the two C-terminal truncated (PjaAI and PjaAII) forms of ${\beta}-agarase$. The PjaAI and the PjaAII were originated from exonuclease L treatment from PjaA by deleting 127 and 182 amino acid residues-encoded nucleic acids at 3' region, respectively. The molecular weights of the purified proteins were 71 kDa, 58 kDa, and 50 kDa on SDS-PAGE, respectively. The $K_m$ value of PjaAI was lower than that of the PjaA, and the catalytic efficiency ($k_{cat}/K_m$) of PjaAI was increased to 5 times. The enzyme of PjaAI retained more than 90% activity at $50^{\circ}C$. In contrast to the PjaAI, the remaining activity of the PjaA was only 20% at the same temperature.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.461-463
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• 2023

MZ세대를 중심으로 자기관리를 열심히 하는 사람들이 증가함에 따라 화장의 기본이 되는 개인 피부톤(퍼스널 컬러)을 찾는 것이 중요시되고 있다. 현재 대다수 사람은 자신에게 어울리는 퍼스널 컬러를 찾기 위해 높은 비용을 지불하여 전문가를 이용하거나 객관적이고 정량화된 기준 없이 오랜 시간을 투자하여 스스로 퍼스널 컬러를 찾는 등 시간과 비용 측면에서의 한계점을 가지고 있다. 본 논문에서는 이를 보완하기 위해 이미지 기반 인공지능 기술(객체 탐지, 객체 분할, BeautyGAN)을 적용하여 데이터 기반의 정량적인 기준을 생성하고, 퍼스널 컬러에 알맞은 화장품 추천 웹 서비스를 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.656-659
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• 2023

정서적 지원 대화를 위한 챗봇 개발 시, 사용자의 챗봇에 대한 사용성 및 대화 적절성을 높이기 위해서는 사용자 감정에 적합한 지원 콘텐츠를 제공하는 것이 중요하다. 이를 위해, 본 논문은 사용자 입력 텍스트의 감정 강도 예측 모델을 제안하고, 사용자 발화 맞춤형 정서적 지원 대화에 적용하고자 한다. 먼저 입력된 한국어 문장에서 키워드를 추출한 뒤, 이를 각성도 (arousal)과 긍정부 정도(valence) 공간에 투영하여 키워드가 내포하는 각성도-긍정부정도에 가장 근접한 감정을 예측하였다. 뿐만 아니라, 입력된 전체 문장에 대한 감정 강도를 추가로 예측하여, 핵심 감정 강도 - 문맥상 감정강도를 모두 추출하였다. 이러한 통섭적 감정 강도 지수들은 사용자 감정에 따른 최적 지원 전략 선택 및 최적 대화 콘텐츠 생성에 공헌할 것으로 기대한다.