• 한국인터넷방송통신학회논문지
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• 제21권5호
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• pp.71-77
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• 2021

최근 이질적인 하드웨어 특성을 고려한 IoT 응용 지원 프레임워크의 효율적인 프로그램 개발이 요구되고 있다. 또한, 인간의 뇌를 모사하여 스스로 학습 및 자율적 컴퓨팅이 가능한 뉴로모픽 아키텍처의 발전으로 하드웨어 지원의 범위가 넓어지고 있다. 하지만 기존 대부분의 IoT 통합개발환경에서는 AI(Artificial Intelligence) 기능을 지원하거나 뉴로모픽 아키텍처와 같은 다양한 하드웨어와 결합된 서비스 지원이 어렵다. 본 논문에서는 2세대 인공 신경망 및 3세대 스파이킹 신경망 모델을 모두 지원하는 AI 컴포넌트 추상화 모델을 설계하고 제안 모델 기반의 자율형 IoT 통합개발환경을 구현하였다. IoT 개발자는 AI 및 스파이킹 신경망에 대한 지식이 없어도 제안 기법을 통해 자동으로 AI 컴포넌트를 생성할 수 있으며 런타임에 따라 코드 변환이 유연하여 개발 생산성이 높다. 제안 기법의 실험을 진행하여 가상 컴포넌트 계층으로 인한 변환 지연시간이 발생할 수 있으나 차이가 크지 않음을 확인하였다.

• 한국정보통신학회논문지
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• 제26권8호
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• pp.1103-1110
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• 2022

AI 관련 산업의 급속한 발전으로 인해 무수히 많은 엣지 디바이스가 실세계에서 동작되고 있고, 이들 디바이스로 구성된 스마트 공간에서 발생하는 데이터가 상상을 초월함으로, 엣지 디비이스가 처리하는 것이 점점 어려워지고 있다. 이러한 문제를 해결하기 위해서 EdgeCPS 기술이 등장하게 되었다. EdgeCPS는 엣지 디바이스와 엣지 서버간 연동과 자원 증강 및 기능 증강을 통하여 AI 응용 서비스를 포함한 다양한 응용 서비스의 원활한 수행을 지원하기 위한 기술이다. 따라서, 본 논문에서는 EdgeCPS 플랫폼에 적용 가능한 지식 공유 그래프 기반의 컴포넌트화된 AI 응용 지원 시스템을 제안한다. 지식 공유 그래프는 AI 응용 작성에 필수적인 요소인 학습데이터, 학습된모델, 학습알고리즘, 디바이스 등에 대한 정보를 효과적으로 저장할 수 있도록 설계된다. 그리고 EdgeCPS 플랫폼의 지원 하에서 자원증강 및 기능증강을 손쉽게 변경할 수 있도록 AI 응용이 컴포넌트화 되어 동작한다. AI 응용 지원 시스템은 사용자가 손쉽게 응용을 작성할 수 있고 테스트 해 볼 수 있도록 지식 공유 그래프와 연동되고, 응용에 대한 파이프라인을 통해서 응용의 실행 양상을 사용자에게 시각화를 해 준다.

• 스마트미디어저널
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• 제11권11호
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• pp.32-39
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• 2022

AI 서비스를 제공하는 IoT 응용이 늘어나면서 자율적인 학습 및 추론을 지원하는 다양한 하드웨어와 소프트웨어들이 개발되고 있다. 하지만 하드웨어마다 특성 및 제약조건이 상이하여 IoT 응용 개발에 어려움이 가중됨에 따라 통합된 플랫폼의 개발이 요구되고 있다. 본 논문에서는 IoT 기술뿐만 아니라 인공 신경망 및 스파이킹 신경망 기반의 컴포넌트를 오픈 플랫폼과 호환되도록 자동 생성하는 도구를 제안한다. 제안하는 컴포넌트 자동 생성 도구는 IoT 및 AI의 가상 컴포넌트 계층을 통해 다양한 하드웨어의 특성에 맞는 컴포넌트 생성을 용이하게 하고 자동으로 오픈 플랫폼에 적용할 수 있도록 지원한다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume I
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• pp.236-238
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• 2006

An algorithm for detection of yellow sand aerosols has been developed with infrared bands from Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-functional Transport Satellite-1 Replacement (MTSAT-1R) data. The algorithm is the hybrid algorithm that has used two methods combined together. The first method used the differential absorption in brightness temperature difference between $11{\mu}m$ and $12{\mu}m$ (BTD1). The radiation at 11 ${\mu}m$ is absorbed more than at 12 ${\mu}m$ when yellow sand is loaded in the atmosphere, whereas it will be the other way around when cloud is present. The second method uses the brightness temperature difference between $3.7{\mu}m$ and $11{\mu}m$ (BTD2). The technique would be most sensitive to dust loading during the day when the BTD2 is enhanced by reflection of $3.7{\mu}m$ solar radiation. We have applied the three methods to MTSAT-1R for derivation of the yellow sand dust and in conjunction with the Principle Component Analysis (PCA), a form of eigenvector statistical analysis. As produced Principle Component Image (PCI) through the PCA is the correlation between BTD1 and BTD2, errors of about 10% that have a low correlation are eliminated for aerosol detection. For the region of aerosol detection, aerosol index (AI) is produced to the scale of BTD1 and BTD2 values over land and ocean respectively. AI shows better results for yellow sand detection in comparison with the results from individual method. The comparison between AI and OMI aerosol index (AI) shows remarkable good correlations during daytime and relatively good correlations over the land.

• International Journal of Advanced Culture Technology
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• 제9권4호
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• pp.329-344
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• 2021

AI assistant, a software interface designed to interact with a user in a natural way and perform specific tasks on the user's behalf, receives increasing attention from both scholars and practitioners. While most of the literatures explain about technical aspects, little is known about the social and psychological factors that intimately influence consumers when using it. This study sheds light on the reason people use AI assistant and how perceived values influence on intention of continuous usage. A total of 361 AI assistant users participated in an online survey, and all were recruited from a major online panel in South Korea. The results from the principal component analysis suggest five social and psychological motives: self-expression, quality of life, entertainment, information, and compatibility. In addition, perceived values, informativeness, entertainment, and trustworthiness, positively predict the intention to use AI assistant. This research provides theoretical contributions from finding motivations of AI assistant usage and from the effects of perceived values on the intention to use it. Practical implications should not be overlooked in this ever-expanding AI industry.

• 열처리공학회지
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• 제5권3호
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• pp.179-185
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• 1992

The shape memory effect results from the martensite transfomation of each individual grain. Thus it is necessary to study the texture and its variation. In this study the change of texture during thermal cycling and it's effect on shape memory ability are investigated. The major component of the rolling texture in the parent phase is identified (001) [110], and minor components are (112) [110], (111) [112], {hkl}<100> fiber texture is developed at $45^{\circ}$ from rolling direction. In the case of martensite phase, it is estimated that the major component is (011) [100] and the minor components are (105) [501], (010) [101] and (100) [001]. According to thermal cycling. severity of texture, especially (001) [110] component in parent phase and (011) [100] component in martensite phase are increased. The shape memory ability is increased with increase of thermal cycles and also increased as the direction of specimen approach to $45^{\circ}$ from rolling direction. After first thermal cycling the temperature of transformation can be define clearly and Ms and As are raised by thermal cycling.

• 대한원격탐사학회지
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• 제22권5호
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• pp.403-406
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• 2006

An algorithm for detection of yellow sand aerosols has been developed with infrared bands. This algorithm is a hybrid algorithm that has used two methods combined. The first method used the differential absorption in brightness temperature difference between $11{\mu}m\;and\;12{\mu}m\;(BTD1)$. The radiation at $11{\mu}m$ is absorbed more than at $12{\mu}m$ when yellow sand is loaded in the atmosphere, whereas it will be the other way around when cloud is present. The second method uses the brightness temperature difference between $3.7{\mu}m\;and\;11{\mu}m(BTD2)$. This technique is sensitive to dust loading, which the BTD2 is enhanced by reflection of $3.7{\mu}m$ solar radiation. First the Principle Component Analysis (PCA), a form of eigenvector statistical analysis from the two methods, is performed and the aerosol pixel with the lowest 10% of the eigenvalue is eliminated. Then the aerosol index (AI) from the combination of BTD 1 and 2 is derived. We applied this method to Multi-functional Transport Satellite-l Replacement (MTSAT-1R) data and obtained that the derived AI showed remarkably good agreements with Ozone Mapping Instrument (OMI) AI and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth.

• The Journal of Asian Finance, Economics and Business
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• 제9권5호
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• pp.225-237
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• 2022

The term "artificial intelligence" is considered a component of sophisticated technological developments, and several intelligent tools have been developed to assist organizations and entrepreneurs in making business decisions. Artificial intelligence (AI) is defined as the concept of transforming inanimate objects into intelligent beings that can reason in the same way that humans do. Computer systems can imitate a variety of human intelligence activities, including learning, reasoning, problem-solving, speech recognition, and planning. This study's objective is to provide responses to the questions: Which factors should be taken into account while deciding whether or not to use AI applications? What role do these elements have in AI application adoption? However, this study proposes a framework to explore the significance and relation of success factors to AI adoption based on the technology-organization-environment model. Ten critical factors related to AI adoption are identified. The framework is empirically tested with data collected by mail surveying organizations in Vietnam. Structural Equation Modeling is applied to analyze the data. The results indicate that Technical compatibility, Relative advantage, Technical complexity, Technical capability, Managerial capability, Organizational readiness, Government involvement, Market uncertainty, and Vendor partnership are significantly related to AI applications adoption.

• 국제초고층학회논문집
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• 제12권3호
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• pp.203-208
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• 2023

The implementation of artificial intelligence (AI) design for tall building structures is an essential solution for addressing critical challenges in the current structural design industry. Generative AI technology is a crucial technical aid because it can acquire knowledge of design principles from multiple sources, such as architectural and structural design data, empirical knowledge, and mechanical principles. This paper presents a set of AI design techniques for building structures based on two types of generative AI: generative adversarial networks and graph neural networks. Specifically, these techniques effectively master the design of vertical and horizontal component layouts as well as the cross-sectional size of components in reinforced concrete shear walls and frame structures of tall buildings. Consequently, these approaches enable the development of high-quality and high-efficiency AI designs for building structures.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2001년도 추계학술대회(한국공작기계학회)
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• pp.355-359
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• 2001

In this study, experiments were carried out to investigate the characteristics of grinding and wear process of diamond wheel grinding ceramic materials. Normal component of grinding resistance of $AI_2O_3$ was less then that of $Si_3N_4$ and $ZrO_2$. It is because the resistance for grain shedding is less then that for layer formation. For the case of $Si_3N_4$ and $ZrO_2$, as the grain mesh number of wheel increases, the surface roughness decreases. For the case of $AI_2O_3$, the surface roughness does not decreases. For the case of $Si_3N_4$ and $ZrO_2$, grinding is carried out by abrasive wear processes. For the case of $AI_2O_3$, grinding is carried out by grain shedding process.