• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.323-332
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• 2022

As various services using AI technology are being developed, much attention is being paid to AI service production. Recently, AI technology is acknowledged as one of ICT services, a lot of research is being conducted for general-purpose AI service production. In this paper, I describe the research results in terms of systems for AI service production, focusing on the distribution and production of machine learning models, which are the final steps of general machine learning development procedures. Three different Ubuntu systems were built, and experiments were conducted on the system, using data from 2017 validation COCO dataset in combination of different AI models (RFCN, SSD-Mobilenet) and different communication methods (gRPC, REST) to request and perform AI services through Tensorflow serving. Through various experiments, it was found that the type of AI model has a greater influence on AI service inference time than AI machine communication method, and in the case of object detection AI service, the number and complexity of objects in the image are more affected than the file size of the image to be detected. In addition, it was confirmed that if the AI service is performed remotely rather than locally, even if it is a machine with good performance, it takes more time to infer the AI service than if it is performed locally. Through the results of this study, it is expected that system design suitable for service goals, AI model development, and efficient AI service production will be possible.

• Journal of Radiation Protection and Research
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• v.17 no.1
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• pp.43-56
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• 1992

As one of the methods to ameliorate the ray effects which are the nature of anomalous computational effects due to the discretization of the angular variable in discrete ordinates approximations, a computational program, named TWODET (TWO dimensional Discrete Element Transport), has developed in 2 dimensional cartesian coordinates system using the discrete elements method, in which the discrete angle quadratures are steered by the spatially dependent angular fluxes. The results of the TWODET calculation with K-2, L-3 discrete angular quadratures, in the problem of a centrally located, isotropically emitting flat source in an absorbing square, are shown to be more accurate than that of the DOT 4.3 calculation with S-10 full symmetry angular quadratures, in remedy of the ray effect at the edge flux distributions of the square. But the computing time of the TWODET is about 4 times more than that of the DOT 4.3. In the problem of vacuum boundaries just outside of the source region in an absorbing square, the results of the TWODET calculation are shown severely anomalous ray effects, due to the sudden discontinuity between the source and the vacuum, like as the results of the DOT 4.3 calculation. In the probelm of an external source in an absorbing square in which a highly absorbing medium is added, the results of the TWODET calculation with K-3, L-4 show a good ones like as, somewhat more than, that of the DOT 4.3 calculation with S-10.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.189-198
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• 2024

AI is accepted not only in the private sector but also in the defense sector as a cutting-edge technology that must be introduced for the development of national defense. In particular, artificial intelligence has been selected as a key task in defense science and technology innovation, and the importance of data is increasing. As the national defense department shifts from a closed data policy to data sharing and activation, efforts are being made to secure high-quality data necessary for the development of national defense. In particular, we are promoting a review of the business budget system to secure data so that related procedures can be improved to reflect the unique characteristics of AI and big data, and research and development can begin with sufficient large quantities and high-quality data. However, there is a need to establish standardization and quality standards for structured data and unstructured data at the national defense level, but the defense department is still proposing standardization and quality standards for structured data, so this needs to be supplemented. In this paper, we propose an unstructured data set standard format for defense unstructured data sets, which are most needed in defense artificial intelligence, and based on this, we propose a standardization method for defense unstructured data sets.