• Journal of KIISE:Computing Practices and Letters
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• v.15 no.10
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• pp.724-739
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• 2009

This paper suggests the energy-efficient message delivery scheme and the software platform which exploits the multiple network interfaces of the mobile terminals and GPS in the current mobile devices. The mobile terminals determine the delivery method among 'direct', 'indirect', and 'WAN' based on the position information of itself and other terminals. 'Direct' method sends a message directly to the target terminal using local RAT. 'Indirect' method extends the service area by exploiting intermediate terminals as relay node. If the target terminal is too far to reach through 'direct' or 'indirect' method, the message is sent using wireless WAN technology. Our proposed scheme exploits the position information and, thus, power consumption is drastically reduced in determining handover time and direction. Network simulation results show that our proposed delivery scheme improves the message transfer efficiency and the handover detection latency. We implemented a message platform in a smart phone realizing the proposed delivery scheme. We compared our platform with other typical message platforms from energy efficiency aspect by observing the real power consumption and applying the mathematical modeling. The comparison results show that our platform requires significantly less power.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.129-137
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• 2009

Operation of intelligent transport systems technologies in transportation networks and more detailed analysis give rise to necessity of dynamic traffic analysis model. Existing static models describe network state in average. on the contrary, dynamic traffic analysis model can describe the time-dependent network state. In this study, a dynamic traffic model for the expressway system using FTMS data is developed. Time-dependent origin-destination trip tables for nationwide expressway network are constructed using TCS data. Computation complexity is critical issue in modeling nationwide network for dynamic simulation. A subarea analysis model is developed which converts the nationwide O-D trip tables into subarea O-D trip tables. The applicability of the proposed model is tested under various scenario. This study can be viewed as a starting point of developing deployable dynamic traffic analysis model. The proposed model needs to be expanded to include arterial as well without critical computation burden.

• Korean Journal of Ecology and Environment
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• v.38 no.4 s.114
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• pp.445-453
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• 2005

Relative importance between bottom-up and top-down controls on phytoplankton dynamics was investigated in the Juam Reservoir, Chonnam based on the results from statistical analyses including regression and artificial neural network (ANN) modeling. Effects of nutrients on size-structured phytoplankton dynamics were explored by simple linear regression analysis and relative importance between bottom-up and top-down controls was estimated based on results from the artificial neural network analyses. Although there is a limitation in determining direct grazing effects since chlorophyll a : pheopigments ratios, indirect index for grazing activity rather than grazing rates or herbivores biomass were used, the results from regression analysis showed that nutrients especially orthophosphates were positively correlated with the phytoplankton biomass and chlorophyll a : pheopigments ratios were also positively correlated with the phytoplankton biomass at lower coefficient of determination ($r^2$) compared to orthophosphates. The simulation results from ANN suggested that the bottom-up mechanisms including water temperature and availability of nutrients, especially orthophosphates were more important than top-down mechanisms such as grazing in the phytoplankton dynamics.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.9
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• pp.528-535
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• 2020

Recently, the protection coordination method of DC systems has been presented because renewable energy and distributed resources are being installed and operated in distribution systems. On the other hand, it is difficult to detect ground faults because there is no significant difference compared to a steady-state current in ungrounded IT systems, such as DC load networks and urban railways. Therefore, this paper formulates the detection principle of IMD (Insulation Monitoring Device) to use it as a protection coordination device in a DC system. Based on the signal injection method of IMD, which is analyzed by a wavelet transform, this paper presents an algorithm of detecting ground faults in a DC system in a fast and accurate manner. In addition, this paper modeled an IMD and an ungrounded DC system using the PSCAD/EMTDC S/W and performed numerical analysis of a wavelet transform with the Matlab S/W. The simulation results of a ground fault case in an ungrounded DC system showed that the proposed algorithm and modeling are useful and practical tools for detecting a ground fault in a DC system.

• Journal of Software Assessment and Valuation
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• v.17 no.2
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• pp.109-116
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• 2021

Traditionally, the engineering field has been dominated by face-to-face education focused on experimental practice, but demand for online learning has soared due to the rapid development of IT technology and Internet communication networks and recent changes in the social environment such as COVID-19. In order for efficient online education to be conducted in the engineering field, where the proportion of experimental practice is relatively high compared to other fields, virtual laboratory practice content that can replace actual experimental practice is very necessary. In this study, we developed a line tracer model and a virtual experimental software to simulate it for efficient online learning of microprocessor applications that are essential not only in the electric and electronic field but also in the overall engineering field where IT convergence takes place. In the developed line tracer model, the user can set various hardware parameter values in the desired form and write the software in assembly language or C language to test the operation on the computer. The developed line tracer virtual experimental software has been used in actual classes to verify its operation, and is expected to be an efficient virtual experimental practice tool in online non-face-to-face classes.

• Korean Chemical Engineering Research
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• v.57 no.2
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• pp.274-282
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• 2019

In recent years, big data analysis has been expanded to include automatic control through reinforcement learning as well as prediction through modeling. Research on the utilization of image data is actively carried out in various industrial fields such as chemical, manufacturing, agriculture, and bio-industry. In this paper, we applied NASNet, which is an AutoML reinforced learning algorithm, to DeepU-Net neural network that modified U-Net to improve image semantic segmentation performance. We used BRATS2015 MRI data for performance verification. Simulation results show that DeepU-Net has more performance than the U-Net neural network. In order to improve the image segmentation performance, remove dropouts that are typically applied to neural networks, when the number of kernels and filters obtained through reinforcement learning in DeepU-Net was selected as a hyperparameter of neural network. The results show that the training accuracy is 0.5% and the verification accuracy is 0.3% better than DeepU-Net. The results of this study can be applied to various fields such as MRI brain imaging diagnosis, thermal imaging camera abnormality diagnosis, Nondestructive inspection diagnosis, chemical leakage monitoring, and monitoring forest fire through CCTV.