• 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.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.2
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• pp.60.2-60.2
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• 2014

Hierarchical structure of star-forming regions is widespread and may be characteristic of all star formation. We studied the hierarchical structure of star-forming regions in the Local Group galaxies (M31, M33, Phoenix, Pegasus, Sextans A, Sextans B, WLM). The star-forming regions were selected from Galaxy Evolution Explorer (GALEX) far-UV imaging in various detection thresholds for investigating hierarchical structure. We examined the spatial distribution of the hot massive stars within star-forming regions from Hubble Space Telescope (HST) multi-band photometry. Small compact groups arranged within large complexes. The cumulative mass distribution follows a power law. The results allow us to understand the hierarchical structure of star formation and recent evolution of the Local Group galaxies.

• Journal of the Korean association of regional geographers
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• v.14 no.4
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• pp.367-381
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• 2008

The purpose of this paper is to analyze the change in the distribution and hierarchical structure of commercial land use. Tools for analyzing the spatial structure are index of concentration, coefficient of localization and location quotient. This research is applied to Daegu, focusing on the period 1985-2003. During the 1985-2003 period, the growth rate of commercial land use volume amounted to 330%, compared with a population growth rate of 118%. The biggest component of commercial land use is the retail sector. As the lodging, medical, transportation-warehouse and entertainment sectors have the propensity for concentration and comprise the specialized commercial areas in the suburbs, the other sectors arc evenly distributed. Jung-gu has functioned as a downtown core in the hierarchical structure of commercial areas. So, in the structure of commercial land use, Daegu has continued to be a single nuclear structure. But, Dongdaegu Station influence area has evolved into the second order center since 2000. This is the sign of change in spatial structure from single-nuclear pattern to multi nuclear pattern.

• Spatial Information Research
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• v.20 no.4
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• pp.93-101
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• 2012

Due to complexity of indoor space, the demand of Location Based Services (LBS) in indoor space is increasing as well as outdoor. However, it includes privacy problems of exposing personal location. Location K-anonymity technology is a method to solve the privacy problems with cloaking their locations by Anonymized Spatial Region(ASR). It guarantees K users within a region containing the location of a given user. However previous researches have dealt the problems based on Euclidean distance in outdoor space, and cannot be applied in indoor space where there are constraints of movement such as walls. For this reason, we propose in this paper a K-anonymity for cloaking indoor location in consideration of structures and representation of indoor space. The basic concept of our approach is to introduce a hierarchical structure as ASR for including K-1 users for cloaking their locations. We also proposed a cost model by K and attributes of hierarchical structure to analyze the performance of the method.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.781-790
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• 1999

In this paper we consider estimation of cancer incidence rates for local areas. The raw estimates usually are based on small sample sizes and hence are usually unreliable. A hierarchical Bayes generalized linear model is used which connects the local areas thereby enabling one to 'borrow strength' Random effects with pairwise difference priors model the spatial structure in the data. The methods are applied to cancer incidence estimation for census tracts in a certain region of the state of New York.

• Journal of the Korean Data and Information Science Society
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• v.15 no.1
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• pp.83-94
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• 2004

In this study we analyze categorical data by means of spatial statistics and echelon analysis. To do this, we first determine the hierarchical structure of a given contingency table by using echelon dendrogram then, we detect candidates of hotspots given as the top echelon in the dendrogram. Next, we evaluate spatial scan statistics for the zones of significantly high or low rates based on the likelihood ratio. Finally, we detect hotspots of any size and shape based on spatial scan statistics.

• The Transactions of the Korea Information Processing Society
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• v.3 no.1
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• pp.63-76
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• 1996

In this paper, we propose a new dynamic spatial index structure, called PR -file, for handling spatial objects and the modified hierarchical variance which measures the degree of spatial locality at each level. Under the assumption that a multidimensional search space has a circular domain, PR-file uses the modified hierarchical variance for clustering spatially adjacent objects. The insertion and splitting algorithms of PR_file preserve and index which has a low hierarchical variance regardless of object distributions. The simulation result shows that PR- file has a high hit ratio during a retrieval of objects by using an index with low hierarchical variance. And it shows a characteristic that the larger the bucket capacity, the higher the bucket utilization.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.4
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• pp.179-187
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• 2019

This study aims to investigate the effects of market commonality and resource similarity on price competition and the recursive consequences in the Korean lodging market. Price comparison among hotels in the same geographic market has been facilitated through the development of information technology, rendering little search cost of consumers. While the literature implies the heterogeneous price attack and response among hotels, a limited number of empirical researches focus on the asymmetric and recursive pattern in the competitive dynamics. This study empirically examines the price interactions in the Korean lodging market based on the theoretical framework of competitive price interactions and countervailing power. Demonstrating superiority to the spatial lag model and the ordinary least squares in the estimation, the results from spatial error model suggest that the hotels with longer operational history pose an asymmetric impact on the price of the newer hotels. The asymmetry is also found in chain hotels over the independent, further implying the possibility of predatory pricing. The findings of this study provide the evidence of a hierarchical structure in the price competition, with different countervailing power by the resources of the hotels. Theoretical and managerial implications are discussed, with suggestions for future study.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.8
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• pp.1227-1237
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• 1989

In this paper, we propose a lossless progressive image transmission scheme using hierarchical pyramid structure and classified vector quantizer in DCT domain. By adopting DCT to the hierarchical pyramid signals, we can reduce the spatial redundance. Moreover, the DCT coefficients can be encoded efficiently by using classified vector quantizer in DCT domain. The classifier is simply based on the variance of a subblock. Also, the mirror set of training set of images can improve the robustness of codebooks. Progressive image transmission can be achieved through following processes: from top to bottom level of planes in a pyramid, and from high to low AC variance class in a plane. Some simulation results with real images show that the proposed coding scheme yields a good performance at below 0.3 bpp and an excellent result at 0.409 bpp. The proposed coding scheme is well suited for lossless progressive image transmission as well as image data compression.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1614-1632
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• 2017

Bag of visual words is a popular model in human action recognition, but usually suffers from loss of spatial and temporal configuration information of local features, and large quantization error in its feature coding procedure. In this paper, to overcome the two deficiencies, we combine sparse coding with spatio-temporal pyramid for human action recognition, and regard this method as the baseline. More importantly, which is also the focus of this paper, we find that there is a hierarchical structure in feature vector constructed by the baseline method. To exploit the hierarchical structure information for better recognition accuracy, we propose a tree regularized classifier to convey the hierarchical structure information. The main contributions of this paper can be summarized as: first, we introduce a tree regularized classifier to encode the hierarchical structure information in feature vector for human action recognition. Second, we present an optimization algorithm to learn the parameters of the proposed classifier. Third, the performance of the proposed classifier is evaluated on YouTube, Hollywood2, and UCF50 datasets, the experimental results show that the proposed tree regularized classifier obtains better performance than SVM and other popular classifiers, and achieves promising results on the three datasets.