• Journal of KIISE:Computer Systems and Theory
• /
• v.34 no.11
• /
• pp.589-598
• /
• 2007

In the group communication which has multiple data streams and various access privileges, it is necessary to provide group access control. The group members having the same access privilege are classified into one class, and the classes form a hierarchy based on the access relations. Then each class is assigned to a secret key. In the previous schemes, a single logical key graph is constructed from the hierarchy and each member always holds all secret keys of the classes he can access in the proactive manner. Thus, higher-privileged members hold more keys then lower-privileged members. However, if the hierarchy is large, each member manages too many keys and the size of multicast message in rekeying increases in proportion to the size of the hierarchy. Moreover, most of the members access a small portion of multiple data streams simultaneously. Therefore, it is redundant to receive rekeying message and update the keys in which he is not currently interested. In this paper, we present a new key management scheme that takes a reactive approach in which each member obtains the key of a data stream only when he wants to access the stream. Each member holds and updates only the key of the class he belongs. If he wants to get the key of other class, he derives it from his key and the public parameter. Proposed scheme considerable reduces the costs for rekeying, especially in the group where access relations are very complex and the hierarchy is large. Moreover, the scheme has another advantage that it easily reflects the change of access relations.

• The KIPS Transactions:PartB
• /
• v.15B no.5
• /
• pp.457-464
• /
• 2008

In the real-world operational environment, most of text classification systems have the problems of insufficient training documents and no prior knowledge of feature space. In this regard, $Na{\ddot{i}ve$ Bayes is known to be an appropriate algorithm of operational text classification since the classification model can be evolved easily by incrementally updating its pre-learned classification model and feature space. This paper proposes the improving technique of $Na{\ddot{i}ve$ Bayes classifier through feature weighting strategy. The basic idea is that parameter estimation of $Na{\ddot{i}ve$ Bayes considers the degree of feature importance as well as feature distribution. We can develop a more accurate classification model by incorporating feature weights into Naive Bayes learning algorithm, not performing a learning process with a reduced feature set. In addition, we have extended a conventional feature update algorithm for incremental feature weighting in a dynamic operational environment. To evaluate the proposed method, we perform the experiments using the various document collections, and show that the traditional $Na{\ddot{i}ve$ Bayes classifier can be significantly improved by the proposed technique.

• Korea journal of population studies
• /
• v.33 no.2
• /
• pp.85-111
• /
• 2010

South Korea has experienced a rapid fertility decline and notable mortality improvement. As the drop in TFR was quicker and greater in terms of tempo and magnitude, it cast a new challenge of population projection - how to improve the forecasting accuracy in the country with a super-low fertility pattern. This study begin with the current status of the national population projection as implemented by Statistics Korea by comparing the 2009 interim projection with the 2006 official national population projection. Secondly, this study compare the population projection system including projection agencies, projection horizons, projection intervals, the number of projection scenarios, and the number of assumptions on fertility, mortality and international migration among super-low fertility countries. Thirdly we illustrate a stochastic population projection for Korea by transforming the population rates into one parameter series. Finally we describe the future challenges of the national population projection, and propose the projection scenarios for the 2011 official population projection. To enhance the accuracy, we suggest that Statistics Korea should update population projections more frequently or distinguish them into short-term and long-term projections. Adding more than four projection scenarios including additional types of "low-variant"fertility could show a variety of future changes. We also expect Statistics Korea topay more attention to the determination of a base population that should include both national and non-national populations. Finally we hope that Statistics Korea will find a wise way to incorporate the ideas underlying the system of stochastic population projection as part of the official national population projection.

• Journal of Korea Water Resources Association
• /
• v.55 no.10
• /
• pp.761-774
• /
• 2022

Accurate hydrologic prediction is essential to analyze the effects of drought, flood, and climate change on flow rates, water quality, and ecosystems. Disentangling the uncertainty of the hydrological model is one of the important issues in hydrology and water resources research. Hydrologic data assimilation (DA), a technique that updates the status or parameters of a hydrological model to produce the most likely estimates of the initial conditions of the model, is one of the ways to minimize uncertainty in hydrological simulations and improve predictive accuracy. In this study, the two ensemble-based sequential DA techniques, ensemble Kalman filter, and particle filter are comparatively analyzed for the daily discharge simulation at the Yongdam catchment using airGRdatassim. The results showed that the values of Kling-Gupta efficiency (KGE) were improved from 0.799 in the open loop simulation to 0.826 in the ensemble Kalman filter and to 0.933 in the particle filter. In addition, we analyzed the effects of hyper-parameters related to the data assimilation methods such as precipitation and potential evaporation forcing error parameters and selection of perturbed and updated states. For the case of forcing error conditions, the particle filter was superior to the ensemble in terms of the KGE index. The size of the optimal forcing noise was relatively smaller in the particle filter compared to the ensemble Kalman filter. In addition, with more state variables included in the updating step, performance of data assimilation improved, implicating that adequate selection of updating states can be considered as a hyper-parameter. The simulation experiments in this study implied that DA hyper-parameters needed to be carefully optimized to exploit the potential of DA methods.