• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.93-98
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• 2021

Due to the development of deep learning and AI, the scale of the model has grown, and it has been integrated into other fields to blend into our lives. However, in environments with limited resources such as embedded devices, it is exist difficult to apply the model and problems such as power shortages. To solve this, lightweight methods such as clouding or offloading technologies, reducing the number of parameters in the model, or optimising calculations are proposed. In this paper, quantization of learned models is applied to ONNX models used in various framework interchange formats, neural network structure and inference performance are compared with existing models, and various module methods for quantization are analyzed. Experiments show that the size of weight parameter is compressed and the inference time is more optimized than before compared to the original model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.786-792
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• 2022

In the cloud computing environment, there has been a lot of research into the Fog/Edge Computing (FEC) paradigm for securing user proximity of application services and computation offloading to alleviate service delay difficulties. The method of predicting dynamic location change patterns of edge devices (moving objects) requesting application services is critical in this FEC environment for efficient computing resource distribution and deployment. This paper proposes an optimal moving pattern extraction algorithm in which variable weights (distance, time, congestion) are applied to selected paths in addition to a support factor threshold for frequency patterns (moving objects) of edge devices. The proposed algorithm is compared to the OPE_freq [8] algorithm, which just applies frequency, as well as the A^* and Dijkstra algorithms, and it can be shown that the execution time and number of nodes accessed are reduced, and a more accurate path is extracted through experiments.

• Journal of Satellite, Information and Communications
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• v.6 no.1
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• pp.37-44
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• 2011

COMS(Chollian) satellite which was launched on June 26, 2010 has three payloads for Ka-band communications, geostationary ocean color imaging and meteorological imaging. In order to make efficient use of the geostationary satellite, a concept of mission operations has been considered from the beginning of the satellite ground control system development. COMS satellite mission operations are classified by daily, weekly, monthly, and seasonal operations. Daily satellite operations include mission planning, command planning and transmission, telemetry processing and analysis, ranging and orbit determination, ephemeris and event prediction, and wheel off-loading set point parameter calculation. As a weekly operation, North-South station keeping maneuver and East-West station keeping maneuver should be performed on Tuesday and Thursday, respectively. Spacecraft oscillator updating parameter should be calculated and uploaded once a month. Eclipse operations should be performed during a vernal equinox and autumnal equinox season. In this paper, operational validations of the major functions in COMS SGCS are presented for the first three month of in-orbit test operations. All of the major functions have been successfully verified and the COMS SGCS will be used for the mission operations of the COMS satellite for 7 years of mission life time and even more.