• Fashion & Textile Research Journal
• /
• v.23 no.6
• /
• pp.790-798
• /
• 2021

With digital technology innovation, increased data access and mobile network use by consumers, products and services are changing toward pursuing differentiated values for personalization, and personalized markets are rapidly emerging in the fashion industry. This study aims to identify trends in digital customized automation technology by deriving types of digital customizing and analyzing cases by type, and to present directions for the development of digital customizing processes and the use of technology in the future. As a research method, a literature study for a theoretical background, a case study for classification and analysis of types was conducted. The results of the study are as follows. The types of digital customizing can be classified into three types: 'cooperative customization', 'selective composition and combination', 'transparent suggestion', and automation technologies shown in each type include 3D printing, 3D virtual clothing, robot mannequin, human automatic measurement program, AR-based fitting service, big data, and AI-based curation function. With the development of digital automation technology, the fashion industry environment is also changing from existing manufacturing-oriented to consumer-oriented, and the production process is rapidly changing with IT and artificial intelligence-based automation technology. The results of this study hope that digital customized automation technology will meet various needs of personalization and customization and present the future direction of digital fashion technology, where fashion brands will expand based on the spread of digital technology.

• International Journal of Advanced Culture Technology
• /
• v.10 no.3
• /
• pp.272-280
• /
• 2022

During last few years, pandemic of COVID-19 has been a global issue. Under the COVID-19, global events have been restricted or canceled to secure public hygiene and safety. Since one of the largest global events is Olympic Games, we selected recent Olympic Games as our case of analysis. Tokyo Olympic Games (TOG) was held in 2021, but it encountered a millennium disaster, the pandemic of COVID-19. In such a special period, it is of great significance to explore the emotional tendency of global views before and TOG via artificial intelligence. This paper vastly collects the TOG comment data of mainstream websites in South Korea, China, and the United States by implementing crawler program for sentiment analysis (SA). And we use a variety of sentiment analysis models to compare the accuracy of the experimental results, to obtain more reliable SA results. In addition, in the prediction results, to reduce the distortion of opinion by a minority, we introduce an algorithm called "Removing Biased Minority Opinions (RBMO)" and provide how to apply this method to the interpretation domain. Through our method, more authoritative SA results were obtained, which in turn provided a basis for predicting the sentiment tendency of countries around the world in TOG during the COVID-19 epidemic.

• International Journal of Internet, Broadcasting and Communication
• /
• v.14 no.4
• /
• pp.121-131
• /
• 2022

We look at the initial stage of Meta (previous Facebook)'s new metaverse platform and investigate its platform design in pre-launch and ignition life stage. From the Rocket Model (RM)'s theoretical logic, the results reveal that Meta firstly focuses on investing in key content developers by acquiring virtual reality (VR), video, music content firms and offering production support platform of the augmented reality (AR) content, 'Spark AR' last three years (2019~2021) for attracting high-potential developers and users. In terms of three matching criteria, Meta develops an Artificial Intelligence (AI) powered translation software, partners with Microsoft (MS) for cloud computing and AI, and develops an AI platform for realistic avatar, MyoSuite. In 'connect' function, Meta curates the game concept submitted by game developers, welcomes other game and SNS based metaverse apps, and expands Horizon Worlds (HW) on VR devices to PCs and mobile devices. In 'transact' function, Meta offers 'HW Creator Funding' program for metaverse, launches the first commercialized Meta Avatar Store on Meta's conventional SNS and Messaging apps by inviting all fashion creators to design and sell clothing in this store. Mata also launches an initial test of non-fungible token (NFT) display on Instagram and expands it to Facebook in the US. Lastly, regarding optimization, especially in the face of recent data privacy issues that have adversely affected corporate key performance indicators (KPIs), Meta assures not to collect any new data and to make its privacy policy easier to understand and update its terms of service more user friendly.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.20 no.12
• /
• pp.1-7
• /
• 2021

The deep learning image processing technique was used to prevent accidents in lathe work caused by worker negligence. During lathe operation, when the chuck is rotated, it is very dangerous if the operator's hand is near the chuck. However, if the chuck is stopped during operation, it is not dangerous for the operator's hand to be in close proximity to the chuck for workpiece measurement, chip removal or tool change. We used YOLO (You Only Look Once), a deep learning image processing program for object detection and classification. Lathe work images such as hand, chuck rotation and chuck stop are used for learning, object detection and classification. As a result of the experiment, object detection and class classification were performed with a success probability of over 80% at a confidence score 0.5. Thus, we conclude that the artificial intelligence deep learning image processing technique can be effective in preventing incidents resulting from worker negligence in future manufacturing systems.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.27 no.1
• /
• pp.56-74
• /
• 2023

In order to obtain practical and high-quality satellite images containing high-frequency components, a large aperture optical system is required, which has a limitation in that it greatly increases the payload weight. As an attempt to overcome the problem, many multi-aperture optical systems have been proposed, but in many cases, these optical systems do not include high-frequency components in all directions, and making such an high-quality image is an ill-posed problem. In this paper, we use deep learning to overcome the limitation. A deep learning model receives low-quality images as input, estimates the Point Spread Function, PSF, and combines them to output a single high-quality image. We model images obtained from three rectangular apertures arranged in a regular polygon shape. We also propose the Modulation Transfer Function Loss, MTF Loss, which can capture the high-frequency components of the images. We present qualitative and quantitative results obtained through experiments.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.17 no.2
• /
• pp.357-364
• /
• 2022

A pseudo-random number generator (PRNG) is a program used when a large amount of random numbers is needed. It is used to generate symmetric keys in symmetric key cryptography systems, generate public key pairs in public key cryptography or digital signatures, and generate columns used for padding with disposable pads. Cellular Automata (CA), which is useful for specific representing nonlinear dynamics in various scientific fields, is a discrete and abstract computational system that can be implemented in hardware and is applied as a PRNG that generates keys in cryptographic systems. In this paper, I propose an algorithm for synthesizing a programmable 5-neighbor CA based PRNG that can effectively generate a nonlinear sequence using 5-neighbor CA with the radius of the neighboring cell increased by 2.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2021.07a
• /
• pp.195-198
• /
• 2021

COVID-19 전염병은 우리의 일상 생활에 빠르게, 그리고 엄청난 영향을 미쳤다. 현재는 마스크를 착용하는 것이 새로운 평범함이 되었고, 이에 따라 많은 서비스 제공업체들은 고객들에게 그들의 서비스를 이용하기 위해 마스크를 착용하도록 요구하고 있다. 공공 버스도 이에 포함된다. 여러 뉴스 기사에 따르면 마스크를 써 달라는 버스 기사의 부탁에 버스 기사를 폭행한 사건이 여러 번 발생하였다. 이에 기계가 마스크를 쓰지 않은 사람을 가려내고 마스크를 쓰라고 한다면 버스 기사에게 향하는 비이성적 분노가 줄어들 것이라고 생각하였다. 따라서, 본 논문에서는 Keras와 같은 기본적인 기계 학습 패키지를 사용하여 빠르고 정확하게 마스크의 착용여부를 확인할 수 있는 방식을 제안한다. 제안된 방식은 고성능 컴퓨터 및 그래픽카드의 필요없이 CPU에서만 작동하는 마스크 착용 판별프로그렘으로, 추가적으로 알림을 보낼 수 있는 웹사이트와 음성 경고 시스템도 함께 구현하였다. 이 방법은 테스트 데이터셋에서 99.5% 이상의 정확도를 달성했고, GPU가 아닌 CPU에서 6fps 정도의 속도를 지원하여 실생활에 사용될 수 있다.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2022.07a
• /
• pp.389-390
• /
• 2022

4차 산업혁명이 도래하여 사회 전반에서 혁신적인 변화가 일어났다. 이에 따라 2022 개정 교육과정에서는 미래 사회가 요구하는 소양 및 역량 강화를 위하여 인공지능(Artificial Intelligence, AI)·소프트웨어(Software, SW)교육을 비롯한 디지털 기초 소양을 강화하는 것을 개정의 중점으로 설정하였다. 이처럼 SW교육은 정보 관련 교과를 비롯한 타 교과에서도 중요하며 따라서 비 정보과 교사도 SW관련 교육 내용을 이해해야 할 필요가 있다. 본 연구에서는 비 정보과 교사에게 SW연수를 실시하였고, SW교육 수업 전문성의 변화를 살펴보았다. 그 결과 사전 검사에 비해 사후 검사 결과에서 통계적으로 유의한 상승을 확인하였다. 이는 SW연수가 비 정보과 교사의 SW교육 수업 전문성 함양에 긍정적인 영향을 준다는 것을 의미한다. 그러나 본 연구는 단일집단으로 이루어진 실험을 설계하여 실시하였으므로, 이러한 변화가 처치로 인한 것인지 확인할 수 없다는 한계점이 있다. 그러므로 통제 집단 및 실험 집단 선별 과정을 거친 후속 연구 설계가 요구된다.

• Journal of Korea Artificial Intelligence Association
• /
• v.1 no.1
• /
• pp.1-6
• /
• 2023

This study developed models using decision forest, support vector machine, and logistic regression methods to predict and prevent suicidal ideation among Korean adolescents. The study sample consisted of 51,407 individuals after removing missing data from the raw data of the 18th (2022) Youth Health Behavior Survey conducted by the Korea Centers for Disease Control and Prevention. Analysis was performed using the MS Azure program with Two-Class Decision Forest, Two-Class Support Vector Machine, and Two-Class Logistic Regression. The results of the study showed that the decision forest model achieved an accuracy of 84.8% and an F1-score of 36.7%. The support vector machine model achieved an accuracy of 86.3% and an F1-score of 24.5%. The logistic regression model achieved an accuracy of 87.2% and an F1-score of 40.1%. Applying the logistic regression model with SMOTE to address data imbalance resulted in an accuracy of 81.7% and an F1-score of 57.7%. Although the accuracy slightly decreased, the recall, precision, and F1-score improved, demonstrating excellent performance. These findings have significant implications for the development of prediction models for suicidal ideation among Korean adolescents and can contribute to the prevention and improvement of youth suicide.

• Clinical Endoscopy
• /
• v.57 no.4
• /
• pp.434-445
• /
• 2024

Pancreatic cystic lesions (PCLs) have increased in prevalence due to the increased usage and advancements in cross-sectional abdominal imaging. Current diagnostic techniques cannot distinguish between PCLs requiring surgery, close surveillance, or expectant management. This has increased the morbidity and healthcare costs from inappropriately aggressive and conservative management strategies. Endoscopic ultrasound (EUS) needle-based confocal laser endomicroscopy (nCLE) allows for microscopic examination and delineation of the surface epithelium of PCLs. Landmark studies have identified characteristics distinguishing various types of PCLs, confirmed the high diagnostic yield of EUS-nCLE (especially for PCLs with an equivocal diagnosis), and shown that EUS-nCLE helps to change management and reduce healthcare costs. Refining procedure technique and reducing procedure length have improved the safety of EUS-nCLE. The utilization of artificial intelligence and its combination with other EUS-based advanced diagnostic techniques would further improve the results of EUS-based PCL diagnosis. A structured training program and device improvements to allow more complete mapping of the pancreas cyst epithelium will be crucial for the widespread adoption of this promising technology.