• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.299-310
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• 2022

As interest in social network analysis increases, researchers' interest in detecting changes in social networks is also increasing. Changes in social networks appear as structural changes in the network. Therefore, detecting a change in a social network is detecting a change in the structural characteristics of the network. A local change in a social network is a change that occurs in a part of the network. It usually occurs between close neighbors. The purpose of this paper is to propose a procedure to efficiently detect local changes occurring in the network. In this paper, we divide the network into partitioned networks and monitor each partitioned network to detect local changes more efficiently. By monitoring partitioned networks, we can detect local changes more quickly and obtain information about where the changes are occurring. Simulation studies show that the proposed method is efficient when the network size is small and the amount of change is small. In addition, under a fixed overall false alarm rate, when we partition the network into smaller sizes and monitor smaller partitioned networks, it detects local changes better.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.661-676
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• 2017

This study suggests a new method not only to produce the 1 km-resolution seasonal prediction but also to improve the seasonal prediction skill of temperature over South Korea. This method consists of four stages of experiments. The first stage, EXP1, is a low-resolution seasonal prediction of temperature obtained from Pusan National University Coupled General Circulation Model, and EXP2 is to produce 1 km-resolution seasonal prediction of temperature over South Korea by applying statistical downscaling to the results of EXP1. EXP3 is a seasonal prediction which considers the effect of temperature changes according to the altitude on the result of EXP2. Here, we use altitude information from ASTER GDEM, satellite observation. EXP4 is a bias corrected seasonal prediction using genetic algorithm in EXP3. EXP1 and EXP2 show poorer prediction skill than other experiments because the topographical characteristic of South Korea is not considered at all. Especially, the prediction skills of two experiments are lower at the high altitude observation site. On the other hand, EXP3 and EXP4 applying the high resolution elevation data based on remote sensing have higher prediction skill than other experiments by effectively reflecting the topographical characteristics such as temperature decrease as altitude increases. In addition, EXP4 reduced the systematic bias of seasonal prediction using genetic algorithm shows the superior performance for temporal variability such as temporal correlation, normalized standard deviation, hit rate and false alarm rate. It means that the method proposed in this study can produces high-resolution and high-quality seasonal prediction effectively.