• 실천공학교육논문지
• /
• 제15권3호
• /
• pp.589-595
• /
• 2023

인공지능 기술은 기술과 교육을 조합한 에듀테크(EdTech) 분야에서 효율적인 교육 콘텐츠 제공과 개인화된 학습자 환경을 구축 등 새로운 혁신을 이끌고 있다. 본 연구는 최근 발전된 생성형 AI를 활용한 프로그래밍 교육 소프트웨어를 개발한다. 최근 잘 알려진 ChatGPT API 기반으로 프로그래밍 코드 분석에 최적화된 프롬프트를 연동했다. 이외 프로그래밍 소스 코드 학습에 필요한 기능을 UI로 설계하고 AI 챗봇 기반의 질의/응답 템플릿 기능으로 개발하였다. 본 연구는 생성형 인공지능을 활용한 교육 프로그램 개발의 방향성을 제시하고자 한다.

• Journal of information and communication convergence engineering
• /
• 제21권4호
• /
• pp.322-328
• /
• 2023

The number of senior citizens with large bowel obstruction is steadily growing in Korea. Plain radiography was used to examine the severity and treatment of this phenomenon. To avoid examiner subjectivity in radiography readings, we propose an automatic segmentation method to identify fluid-filled areas indicative of large bowel obstruction. Our proposed method applies the Hough transform to locate suspicious areas successfully and applies the possibilistic fuzzy c-means unsupervised learning algorithm to form the target area in a noisy environment. In an experiment with 104 real-world large-bowel obstruction radiographs, the proposed method successfully identified all suspicious areas in 73 of 104 input images and partially identified the target area in another 21 images. Additionally, the proposed method shows a true-positive rate of over 91% and false-positive rate of less than 3% for pixel-level area formation. These performance evaluation statistics are significantly better than those of the possibilistic c-means and fuzzy c-means-based strategies; thus, this hybrid strategy of automatic segmentation of large bowel suspicious areas is successful and might be feasible for real-world use.

• Computers and Concrete
• /
• 제22권4호
• /
• pp.419-437
• /
• 2018

The compressive strength of self-compacting concrete (SCC) containing fly ash (FA) is highly related to its constituents. The principal purpose of this paper is to investigate the efficiency of hybrid fuzzy radial basis function neural network with biogeography-based optimization (FRBFNN-BBO) for predicting the compressive strength of SCC containing FA based on its mix design i.e., cement, fly ash, water, fine aggregate, coarse aggregate, superplasticizer, and age. In this regard, biogeography-based optimization (BBO) is applied for the optimal design of fuzzy radial basis function neural network (FRBFNN) and the proposed model, implemented in a MATLAB environment, is constructed, trained and tested using 338 available sets of data obtained from 24 different published literature sources. Moreover, the artificial neural network and three types of radial basis function neural network models are applied to compare the efficiency of the proposed model. The statistical analysis results strongly showed that the proposed FRBFNN-BBO model has good performance in desirable accuracy for predicting the compressive strength of SCC with fly ash.

• 한국정보통신학회:학술대회논문집
• /
• 한국정보통신학회 2022년도 춘계학술대회
• /
• pp.252-254
• /
• 2022

본 연구는 최근 제주 현대미술가들의 작업을 지속해서 수용하며 활발한 작업을 끌어내고 있다는 점에 연구 배경을 갖고, 제주 미술 작가의 작품과 제주 관광산업에 이바지할 방안에 관해 연구하기 위한 것이다. 이러한 연구 목적을 달성하는 방안으로서 딥드림 제너레이터(Deep Dream Generator) 소프트웨어는 본 연구를 추진하는 데 효과적인 방법이라 판단하였다. 구체적인 연구 과정으로서, 딥드림 제너레이터를 활용해 제주 작가 작품과 딥드림 제너레이터에서 제공하는 저명한 해외 작가들의 작품 각각을 직접 찍은 제주 오름 사진과 합성해 그 결과물을 전시하며, 인공지능을 이용한 제주 오름의 재해석을 시도하고자 한다. 또한, 나온 결과물을 이용해 제주 관광상품에 활용하는 방안을 모색함으로써 제주의 미술 작품과 관광을 활성화할 것으로 기대된다.

• 한국컴퓨터정보학회논문지
• /
• 제29권8호
• /
• pp.77-84
• /
• 2024

본 논문에서는 장애인 예술인의 예술 작품을 전시할 수 있는 안드로이드 플랫폼 기반의 온라인 전시 애플리케이션을 설계하고 구현한다. 이 애플리케이션은 장애인 예술인을 위한 사용자 편의성을 고려한다. 특히 시각 및 청각 장애인을 위한 STT, TTS 기능을 제공한다. 또한, 장애인 예술인의 전시 작품을 위해 회원가입 시 장애 등록증과 등록번호를 활용하여 장애인 인증이 가능하도록 구현함으로써 인증된 장애 예술인만 작품을 전시할 수 있도록 구현한다. 장애인 예술인에 대한 개인정보와 예술 작품에 대한 정보를 저장하는 데이터베이스는 MySQL로 구현한다. 서버 모듈은 RestAPI를 활용하여 JSON 형태의 데이터를 전송하도록 구현한다. 예술 작품에 대한 정보는 데이터 용량이 크기 때문에 서버에 직접 저장하지 않고 Firebase Storage를 활용하여 데이터 용량 제한 없이 저장하도록 구현한다. 이 애플리케이션은 장애 예술인의 전시 공간 부족과 일반 대중과의 소통 부족 문제를 완화할 수 있다.

• International Journal of Advanced Culture Technology
• /
• 제12권1호
• /
• pp.175-181
• /
• 2024

The paper examines the impact of digital media art on urban creative products, analyzing opportunities and challenges in the digital era. It emphasizes the development of urban cultural and creative products, highlighting their significance and future growth potential. The digital media era provides unprecedented innovation opportunities, utilizing advanced tools for efficient design, production, and marketing. Trends like personalization, customization, AI, and big data offer new expressions and market prospects. Cultural products evolve in design, marketing, and sales channels due to digital media, with tools like social media and e-commerce platforms opening new promotion avenues. Case studies illustrate digital media's role in driving innovation and enhancing user experiences. The paper addresses challenges in market competition, copyright, and technological renewal, while recognizing opportunities from AI and big data. The creative industries must adapt and innovate to remain relevant. Looking ahead, urban creative products will evolve under digitalization, relying on digital means to attract consumers and enhance brand value. Cultural products, beyond economic entities, disseminate urban culture and creative spirit. In the digital era, urban creative products demonstrate potential and necessity, prompting a reevaluation of digital technology's role. Through continuous innovation, this field contributes to cultural and economic levels, impacting urban characteristics and heritage. Urban creative products play an increasingly vital role in the global cultural and creative economy.

• Advances in Computational Design
• /
• 제8권1호
• /
• pp.37-59
• /
• 2023

Squat reinforced concrete (SRC) shear walls are a critical part of the structure for both office/residential buildings and nuclear structures due to their significant role in withstanding seismic loads. Despite this, empirical formulae in current design standards and published studies demonstrate a considerable disparity in predicting SRC wall shear strength. The goal of this research is to develop and evaluate hybrid and ensemble artificial neural network (ANN) models. State-of-the-art population-based algorithms are used in this research for hybrid intelligence algorithms. Six models are developed, including Honey Badger Algorithm (HBA) with ANN (HBA-ANN), Hunger Games Search with ANN (HGS-ANN), fitness-distance balance coyote optimization algorithm (FDB-COA) with ANN (FDB-COA-ANN), Averaging Ensemble (AE) neural network, Snapshot Ensemble (SE) neural network, and Stacked Generalization (SG) ensemble neural network. A total of 434 test results of SRC walls is utilized to train and assess the models. The results reveal that the SG model not only minimizes prediction variance but also produces predictions (with R²= 0.99) that are superior to other models.

• International Journal of Contents
• /
• 제15권4호
• /
• pp.1-7
• /
• 2019

As the recent developments of artificial intelligence, particularly machine-learning, impact every aspect of society, they are also increasingly influencing creative fields manifested as new artistic tools and inspirational sources. However, as more artists integrate the technology into their creative works, the issues of diversity and fairness are also emerging in the AI-based creative practice. The data dependency of machine-learning algorithms can amplify the social injustice existing in the real world. In this paper, we present an interactive visualization system for raising the awareness of the diversity and fairness issues. Rather than resorting to education, campaign, or laws on those issues, we have developed a web & ML-based interactive data visualization system. By providing the interactive visual experience on the issues in interesting ways as the form of web content which anyone can access from anywhere, we strive to raise the public awareness of the issues and alleviate the important ethical problems. In this paper, we present the process of developing the ML-based interactive visualization system and discuss the results of this project. The proposed approach can be applied to other areas requiring attention to the issues.

• 센서학회지
• /
• 제29권1호
• /
• pp.19-26
• /
• 2020

Electrocardiogram (ECG) classification has become an essential task of modern day wearable devices, and can be used to detect cardiovascular diseases. State-of-the-art Artificial Intelligence (AI)-based ECG classifiers have been designed using various artificial neural networks (ANNs). Despite their high accuracy, ANNs require significant computational resources and power. Herein, three different ANNs have been compared: multilayer perceptron (MLP), convolutional neural network (CNN), and spiking neural network (SNN) only for the ECG classification. The ANN model has been developed in Python and Theano, trained on a central processing unit (CPU) platform, and deployed on a PYNQ-Z2 FPGA board to validate the model using a Jupyter notebook. Meanwhile, the hardware accelerator is designed with Overlay, which is a hardware library on PYNQ. For classification, the MIT-BIH dataset obtained from the Physionet library is used. The resulting ANN system can accurately classify four ECG types: normal, atrial premature contraction, left bundle branch block, and premature ventricular contraction. The performance of the ECG classifier models is evaluated based on accuracy and power. Among the three AI algorithms, the SNN requires the lowest power consumption of 0.226 W on-chip, followed by MLP (1.677 W), and CNN (2.266 W). However, the highest accuracy is achieved by the CNN (95%), followed by MLP (76%) and SNN (90%).

• Geomechanics and Engineering
• /
• 제34권5호
• /
• pp.507-527
• /
• 2023

Accurately estimation of the geo-mechanical parameters in Artificial Ground Freezing (AGF) is a most important scientific topic in soil improvement and geotechnical engineering. In order for this, one way is using classical and conventional constitutive models based on different theories like critical state theory, Hooke's law, and so on, which are time-consuming, costly, and troublous. The others are the application of artificial intelligence (AI) techniques to predict considered parameters and behaviors accurately. This study presents a comprehensive data-mining-based model for predicting the Young's Modulus of frozen sand under the triaxial test. For this aim, several single and hybrid models were considered including additive regression, bagging, M5-Rules, M5P, random forests (RF), support vector regression (SVR), locally weighted linear (LWL), gaussian process regression (GPR), and multi-layered perceptron neural network (MLP). In the present study, cell pressure, strain rate, temperature, time, and strain were considered as the input variables, where the Young's Modulus was recognized as target. The results showed that all selected single and hybrid predicting models have acceptable agreement with measured experimental results. Especially, hybrid Additive Regression-Gaussian Process Regression and Bagging-Gaussian Process Regression have the best accuracy based on Model performance assessment criteria.