• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.147-153
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• 2023

As it spreads to all industries of artificial intelligence technology, AIaaS equipped with artificial intelligence services is emerging. In particular, non-IT companies are suffering from the absence of software experts, difficulties in training big data models, and difficulties in collecting and analyzing various types of data. AIaaS makes it easier and more economical for users to build a system by providing various IT resources necessary for artificial intelligence software development as well as functions necessary for artificial intelligence software in the form of a service. Therefore, the supply and demand for such cloud-based AIaaS services will increase rapidly. However, the quality of services provided by AIaaS becomes an important factor in what is required as the supply and demand for AIaaS increases. However, research on a comprehensive and practical quality evaluation metric to measure this is currently insufficient. Therefore, in this paper, we develop and propose a usability, replacement, scalability, and publicity metric, which are the four metrics necessary for measuring reusability, based on implementation, convenience, efficiency, and accessibility, which are characteristics of AIaaS, for reusability evaluation among the service quality measurement factors of AIaaS. The proposed metrics can be used as a tool to predict how much services provided by AIaaS can be reused for potential users in the future.

• The Transactions of the Korea Information Processing Society
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• v.6 no.11S
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• pp.3410-3419
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• 1999

Autonomous Intrusion Analysis Agent(AIAA) is Incident Response Team staff's tool that scans, analyses, reports and alerts the traces of intrusion based on system logs and intruder's backdoors inside compromised system by IR staff after security incident is reported to the IR team. AIAA is intelligent to recognize to check out who is intruder from all the user accounts and to report the suspected candidates to the master control system in IR team. IR staff who controls AIAA with master system can pick up an intruder from the candidates reported by AIAA agent and review all related summary reports and details including source host's mane, finger information, all illegal behavior and so on. AIAA is moved to compromised system by the staff to investigate the signature of intrusion along the trace of victim hosts and it is also operated in secret mode to detect the further intrusion. AIAA is alive in all victim systems until the incident is closed and IR staff can control AIAA operation and dialogue with AIAA agent in Web interface.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.97-106
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• 2020

Due to the rapid development and dissemination of 5G communication and IoT technologies, there are increasing demands for big data analysis techniques and service systems. In particular, explosively growing demands on AI technology adoption are also causing high competitions to take advantages of machine/deep-learning models to extract novel values from enormously collected data. In order to adopt AI technology to various research and application domains, it is necessary to prepare high-performance GPU-equipped systems and perform complicated settings to utilze deep learning models. To relieve the efforts and lower the barrier to utilize AI techniques, AIaaS(AI as a service) platform is attracting a great deal of attention as a promising on-line service, where the complexity of preparation and operation can be hidden behind the cloud side and service developers only need to utilize the high-level AI services easily. In this paper, we propose an AIaaS system which can support the creation of AI services based on Docker and OpenFaaS from the registration of models to the on-line operation. We also describe a case study to show how AI services can be easily generated by the proposed system.

• International Journal of Aeronautical and Space Sciences
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• v.13 no.3
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• pp.307-316
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• 2012

Numerical simulations of 3D aircraft configurations are performed in order to understand the effects of turbulence models on the prediction of aircraft's aerodynamic characteristics. An in-house CFD code that solves 3D RANS equations and two-equation turbulence model equations are used. The code applies Roe's approximated Riemann solver and an AF-ADI scheme. Van Leer's MUSCL extrapolation with van Albada's limiter is also adopted. Various versions of Menter's $k-{\omega}$ SST turbulence models as well as Coakley's $q-{\omega}$ model are incorporated into the CFD code. Menter's $k-{\omega}$ SST models include the standard model, the 2003 model, the model incorporating the vorticity source term, and the model containing controlled decay. Turbulent flows over a wing are simulated in order to validate the turbulence models contained in the CFD code. The results from these simulations are then compared with computational results from the $3^{rd}$ AIAA CFD Drag Prediction Workshop. Numerical simulations of the DLR-F6 wing-body and wing-body-nacelle-pylon configurations are conducted and compared with computational results of the $2^{nd}$ AIAA CFD Drag Prediction Workshop. Aerodynamic characteristics as well as flow features are scrutinized with respect to the turbulence models. The results obtained from each simulation incorporating Menter's $k-{\omega}$ SST turbulence model variations are compared with one another.

• 한국전산유체공학회:학술대회논문집
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• 2011.05a
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• pp.420-427
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• 2011

In this study, numerical simulations of transonic aircraft configurations are performed with various turbulence models and the effect of turbulence models on flow separation are examined. A three-dimensional RANS code and three turbulence models are used for the study. The turbulence models incorporated to the code include Menter's ${\kappa}-{\omega}$ model, Coakley's $q-{\omega}$, and Huang and Coakley's ${\kappa}-{\omega}$, model. Using the code, numerical simulations of DLR-F6 configurations obtained from AIAA CFD Drag Prediction Workshop are conducted. Flow separations on the wing-body juncture and the wing lower surface near pylon are observed. and flow features of the regions are compared with experimental data and other numerical results.

• International Journal of Aeronautical and Space Sciences
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• v.12 no.3
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• pp.211-224
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• 2011

This is a written version of an hour-long lecture delivered by the author on June 30, 2011, as Plasmadynamics and Lasers Award Lecture at the AIAA 2011 summer conference in Honolulu, Hawaii. The author proposes that two areas of planetary entry physics be pursued in the future: outer planet aero-capturing and study of aerodynamics of meteoroid entries, both for the purpose of advancing the understanding of the possible extraterrestrial seeding of building blocks of life. For outer planet aero-capturing, the author proposes to develop new shock tube facilities that will produce up to 30 km/s of shock speed without causing photo-ionization of the driven gas by the radiation from the hot driver gas. Regarding meteors, the author proposes to carry out laboratory testing of the Tunguska event and of the seeding of amino acid molecules using a ballistic range which shoots a snowball laden with amino acid molecules toward a water surface.

• Journal of Service Research and Studies
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• v.13 no.4
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• pp.191-205
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• 2023

Artificial Intelligence (AI), especially in the domain of text-generative services, has witnessed a significant surge, with forecasts indicating the AI-as-a-Service (AIaaS) market reaching a valuation of $55.0 Billion by 2028. This research set out to explore the quality dimensions characterizing synthetic text media software, with a focus on four key players in the industry: ChatGPT, Writesonic, Jasper, and Anyword. Drawing from a comprehensive dataset of over 4,000 reviews sourced from a software evaluation platform, the study employed the Latent Dirichlet Allocation (LDA) topic modeling technique using the Gensim library. This process resulted the data into 11 distinct topics. Subsequent analysis involved comparing these topics against established AI service quality dimensions, specifically AICSQ and AISAQUAL. Notably, the reviews predominantly emphasized dimensions like availability and efficiency, while others, such as anthropomorphism, which have been underscored in prior literature, were absent. This observation is attributed to the inherent nature of the reviews of AI services examined, which lean more towards semantic understanding rather than direct user interaction. The study acknowledges inherent limitations, mainly potential biases stemming from the singular review source and the specific nature of the reviewer demographic. Possible future research includes gauging the real-world implications of these quality dimensions on user satisfaction and to discuss deeper into how individual dimensions might impact overall ratings.

• Journal of Advanced Technology Convergence
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• v.1 no.2
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• pp.01-09
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• 2022

Recently, safety issues in companies and public institutions are no longer a task that can be postponed, and when a major safety accident occurs, not only direct financial loss, but also indirect loss of social trust in the company and public institution is greatly increased. In particular, in the case of a fatal accident, the damage is even more serious. Accordingly, as companies and public institutions expand their investments in industrial safety education and prevention, open AI learning model creation technology that enables safety management services without being affected by user behavior in industrial sites where high-risk situations exist, edge terminals System development using inter-AI collaboration technology, cloud-edge terminal linkage technology, multi-modal risk situation determination technology, and AI model learning support technology is underway. In particular, with the development and spread of artificial intelligence technology, research to apply the technology to safety issues is becoming active. Therefore, in this paper, an open cloud platform design method that can support AI model learning for high-risk site safety management is presented.