• Smart Media Journal
• /
• v.12 no.11
• /
• pp.113-124
• /
• 2023

With the advent of 5G and B5G (Beyond 5G) networks, network virtualization technology that can overcome the limitations of existing networks is attracting attention. The purpose of network virtualization is to provide solutions for efficient network resource utilization and various services. Existing heuristic-based VNE (Virtual Network Embedding) techniques have been studied, but the flexibility is limited. Therefore, in this paper, we propose a GNN-based network slicing classification scheme to meet various service requirements and a RL-based VNE scheme for optimal resource allocation. The proposed method performs optimal VNE using an Actor-Critic network. Finally, to evaluate the performance of the proposed technique, we compare it with Node Rank, MCST-VNE, and GCN-VNE techniques. Through performance analysis, it was shown that the GNN and RL-based VNE techniques are better than the existing techniques in terms of acceptance rate and resource efficiency.

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.7
• /
• pp.263-270
• /
• 2021

Reinforcement learning(RL) is the method to find an optimal policy through training. and it is one of popular methods for solving lifesaving and disaster response problems effectively. However, the conventional reinforcement learning method for disaster response utilizes either simple environment such as. grid and graph or a self-developed environment that are hard to verify the practical effectiveness. In this paper, we propose the design of a disaster response RL environment which utilizes the detailed property information of the disaster simulation in order to utilize the reinforcement learning method in the real world. For the RL environment, we design and build the reinforcement learning communication as well as the interface between the RL agent and the disaster simulation. Also, we apply the dimension reduction method for converting non-image feature vectors into image format which is effectively utilized with convolution layer to utilize the high-dimensional and detailed property of the disaster simulation. To verify the effectiveness of our proposed method, we conducted empirical evaluations and it shows that our proposed method outperformed conventional methods in the building fire damage.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.29 no.6
• /
• pp.593-599
• /
• 2011

With increasing user applicability of geospatial data, user demand for manifold and accurate information has increased. The usefulness of these services derives from their combination of the advantages of as-built geospatial data in making new content. There is a spatial inconsistency and shape disagreement in fusing heterogeneous data. Conflation, defined as the combining of information from diverse sources so as to reconcile spatial inconsistencies and shape disagreement, is possible solution to the problem. In this research, we developed the technique for removing shape disagreement between aerial image and road map removed spatial inconsistency in advanced research. The process includes four processes: producing of a road candidate image, extraction of vertices, and generation of a graph by connecting the vertices. We could remove the shape disagreement using the extracted road that was derived from finding the road possible path.

• Journal of the Korean Vacuum Society
• /
• v.15 no.6
• /
• pp.587-593
• /
• 2006

The circuit of the EEFL system and the inverter has been analyzed into the resistance RL, the capacitance C of the EEFL-backlight system, and the inductance of transformer in the inverter. The lamp resistance and capacitance are deter-mined from the phase difference is between the lamp current and voltage and from the Q-V diagram, respectively. The single Lamp of EEFL for 32' LCD-BLU has the resistance of $66\;k\Omega$ and the capacitance of 21.61 pF. The resistance, which is connected by parallel in the 20-EEFLS BLU, is $3.3\;k\Omega$ and the capacitance is 402.1 pF. The matching frequency in the operation of lamp system is noted as $\omega_M=1/\sqrt{L_2C(1-k^2)}$, where $L_2$ is the inductance of secondary coil and k is the coupling coefficient between primary and secondary coil. The lamp current and voltage has maximum value at the matching frequency in the LCD BLU system. The results of analytic solutions are in good agreement with the experimental results.