• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.176-176
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• 2018

This study presents a regional, probabilistic framework for estimating streamflow via spatial scaling in the Great Lakes basin, which is the largest lake system in the world. The framework follows a two-fold strategy including (1) a quadratic-programming based optimization model a priori to explore the model structure, and (2) a time-varying hierarchical Bayesian model based on insights found in the optimization model. The proposed model is developed to explore three innovations in hierarchical modeling for reconstructing historical streamflow at ungaged sites: (1) information of physical characteristics is utilized in spatial scaling, (2) a time-varying approach is introduced based on climate information, and (3) heteroscedasticity in residual errors is considered to improve streamflow predictive distributions. The proposed model is developed and calibrated in a hierarchical Bayesian framework to pool regional information across sites and enhance regionalization skill. The model is validated in a cross-validation framework along with four simpler nested formulations and the optimization model to confirm specific hypotheses embedded in the full model structure. The nested models assume a similar hierarchical Bayesian structure to our proposed model with their own set of simplifications and omissions. Results suggest that each of three innovations improve historical out-of-sample streamflow reconstructions although these improvements vary corrsponding to each innovation. Finally, we conclude with a discussion of possible model improvements considered by additional model structure and covariates.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.673-676
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• 2004

Fast and high-quality document clustering algorithms play an important role in providing data exploration by organizing large amounts of information into a small number of meaningful clusters. In particular, hierarchical clustering provide a view of the data at different levels, making the large document collections are adapted to people's instinctive and interested requires. Many papers have shown that the hierarchical clustering method takes good-performance, but is limited because of its quadratic time complexity. In contrast, K-means has a time complexity that is linear in the number of documents, but is thought to produce inferior clusters. Think of the factor of simpleness, high-quality and high-efficiency, we combine the two approaches providing a new system named CONDOR system [10] with hierarchical structure based on document clustering using K-means algorithm to "get the best of both worlds". The performance of CONDOR system is compared with the VIVISIMO hierarchical clustering system [9], and performance is analyzed on feature words selection of specific topics and the optimum hierarchy depth.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.12 no.6
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• pp.47-58
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• 2002

The access control techniques, which can control unauthorized members to access to multicast service, have not been studied very often while there are a lot of on-going study on secure multicast architecture, multicast key distribution and sender authentication scheme have been studied. Multi level access control scheme in multicast can be used in a remote secure conference or to provide graduated multimedia services to each customers. In fact, multicast network has its own virtual networks according to different security levels. However, Early schemes are not effective when it protects unauthorized access in multi-access network environment. Furthermore this scheme does not provide us with hierarchical access control mechanism. This paper, therefore, proposes hierarchical access control scheme to provide the effectiveness in network layer by security level comparison. And we also suggests hierarchical key distribution scheme for multi level access control in application layer and effective hierarchical key renewal scheme in dynamic multicast environment which is easy to join and leaving the multicast group.

• Proceedings of the Materials Research Society of Korea Conference
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• 2009.11a
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• pp.33.1-33.1
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• 2009

To date, nanostructured tungsten oxides with a variety of stoichiometries, such as WO3, WO2.9, W18O49, and WO2, have been prepared, because they are promising candidates for applications such as gas sensors, photocatalysts, electrochromic devices, and field emission devices. Among them, W18O49 and WO2 have been widely studied due to their outstanding chemical sensing, catalytic, and electron emissive properties. Here we report, for the first time, a one-pot solution-phase route to synthesizing a novel composite hierarchical hollow structure without adding catalysts, surfactants, or templates. The products, consisting of a WO2 hollow core sphere surrounded by a W18O49 nanorod shell (yielding a sea urchin-like structure), were generated as discrete structures via Ostwald ripening. To our knowledge, this type of composite hierarchical core/shell structure has not been reported previously. The morphological evolution and the detailed growth mechanism were carefully studied. We also demonstrate that the size of the hollow urchins is readily tunable by controlling the reactant concentrations.Interestingly, although bulk tungsten oxides are weakly paramagnetic or diamagnetic, the as-prepared products show unusual ferromagnetic behavior atroom temperature. The urchin structures also show a very high Brunauer-Emmet-Teller (BET) surface area, suggesting that they may potentially be applied to chemical sensor or effective catalyst technologies.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.195-200
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• 2002

ART (Adaptive Resonance Theory [1]) neural network and its variations perform non-hierarchical clustering by unsupervised learning. We propose a scheme "arboART" for hierarchical clustering by using several ART1.5-SSS networks. It classifies multidimensional vectors as a cluster tree, and finds features of clusters. The Basic idea of arboART is to use the prototype formed in an ART network as an input to other ART network that has looser distance criteria (Ishihara, et al., [2,3]). By sending prototype vectors made by ART to one after another, many small categories are combined into larger and more generalized categories. We can draw a dendrogram using classification records of sample and categories. We have confirmed its ability using standard test data commonly used in pattern recognition community. The clustering result is better than traditional computing methods, on separation of outliers, smaller error (diameter) of clusters and causes no chaining. This methodology is applied to Kansei evaluation experiment data analysis.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.309-311
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• 2014

The tile-based rendering technique which divides the screen area into tiles of a specific size and creates a 3D graphic model of one tile at a time is used to efficiently utilize limited resources in a 3D graphic pipeline. In this paper, the tiling speed of tile-based rendering was improved by reducing the count of calling lower-levels in the hierarchical tile-based rendering technique. The tiling speed of the proposed Rasterizer is 13.030ms which is 56% faster than 29.614ms of multi-sort tiling and 24% faster than 17.208ms of the conventional hierarchical tiling technique.

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1507-1510
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• 2003

We propose UPGMA(Unweighted Pair Group Method using Average distance) as hierarchical clustering to detect abrupt shot changes using multiple features such as pixel-by-pixel difference, global and local histogram difference. Conventional $\kappa$-means algorithm which is a method of the partitional clustering, has to select an efficient initial cluster center adaptively UPGMA that we propose, does not need initial cluster center because of agglomerative algorithm that it starts from each sample for clusters. And UPGMA results in stable performance. Experiment results show that the proposed algorithm works not only well but also stably.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04a
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• pp.706-708
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• 2002

DNA 마이크로어레이 실험으로 나온 데이터들을 클러스터링하는 것은 유전자의 기능과 유전자의 네트워크를 파악해 나가는데 도움을 주게 된다. 계층적 클러스터링(hierarchical clustering) 방법은 그러한 실험 분석에서 가장 보편적으로 사용되는 방법이다. 본 논문에서는 계층적 클러스터링을 통해서 나온 결과 트리에 대해서, 트리의 리프 노드들을 재배열함으로써, 인접한 리프 노드들간의 거리의 종합이 최소가 되도록 하는 문제인 리프오더링 방법을 다루었고, 새로운 리프오더링 알고리즘을 제안하였다. 그리고, 이를 포함한 여러 리프오더링 방법들에 대한 실험 및 생물학적인 분석을 하였다.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2004.07a
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• pp.264-264
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• 2004

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.95-98
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• 2001

A hierarchical structure for the cooperative control of multiple mobile robots using coordinates of objects obtained from vision system is proposed. The order-level perceives environments represented by workspace sets. The algorithm selects an object to be moved using an object discrimination part and determines the robot actions. The action-level generates a trajectory of each wheel velocity of robot. The simulation results show the effectiveness of the proposed algorithm.