• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.71-77
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• 2021

Recently, there is a demand for efficient program development of an IoT application support frameworks considering heterogeneous hardware characteristics. In addition, the scope of hardware support is expanding with the development of neuromorphic architecture that mimics the human brain to learn on their own and enables autonomous computing. However, most existing IoT IDE(Integrated Development Environment), it is difficult to support AI(Artificial Intelligence) or to support services combined with various hardware such as neuromorphic architectures. In this paper, we design an AI component abstract model that supports the second-generation ANN(Artificial Neural Network) and the third-generation SNN(Spiking Neural Network), and implemented an autonomous IoT IDE based on the proposed model. IoT developers can automatically create AI components through the proposed technique without knowledge of AI and SNN. The proposed technique is flexible in code conversion according to runtime, so development productivity is high. Through experimentation of the proposed method, it was confirmed that the conversion delay time due to the VCL(Virtual Component Layer) may occur, but the difference is not significant.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1103-1110
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• 2022

Due to the rapid development of AI-related industries, countless edge devices are working in the real world. Since data generated within the smart space consisted of these devices is beyond imagination, it is becoming increasingly difficult for edge devices to process. To solve this issue, EdgeCPS has appeared. EdgeCPS is a technology to support harmonious execution of various application services including AI applications through interworking between edge devices and edge servers, and augmenting resources/functions. Therefore, we propose a knowledge-sharing graph-based componentized AI application support system applicable to the EdgeCPS platform. The graph is designed to effectively store information which are essential elements for creating AI applications. In order to easily change resource/function augmentation under the support of the EdgeCPS platform, AI applications are operated as components. The application support system is linked with the knowledge graph so that users can easily create and test applications, and visualizes the execution aspect of the application to users as a pipeline.

• Smart Media Journal
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• v.11 no.11
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• pp.32-39
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• 2022

As IoT applications that provide AI services increase, various hardware and software that support autonomous learning and inference are being developed. However, as the characteristics and constraints of each hardware increase difficulties in developing IoT applications, the development of an integrated platform is required. In this paper, we propose a tool for automatically generating components based on artificial neural networks and spiking neural networks as well as IoT technologies to be compatible with open platforms. The proposed component automatic generation tool supports the creation of components considering the characteristics of various hardware devices through the virtual component layer of IoT and AI and enables automatic application to open platforms.

• Proceedings of the KSRS Conference
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• v.1
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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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• v.9 no.4
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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.

• Journal of the Korean Society for Heat Treatment
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• v.5 no.3
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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.

• Korean Journal of Remote Sensing
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• v.22 no.5
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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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• v.9 no.5
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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.

• International Journal of High-Rise Buildings
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• v.12 no.3
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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.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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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.