• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.260-262
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• 2004

Spatial database system provides many query types and most of them are required frequent disk I/O and much CPU time. k-NN search is to find k-th closest object from the query point and up to now, several k-NN search methods have been proposed. Among these, MINMAX distance method has an aim not to visit unnecessary node by applying pruning technique. But this method access more disk than necessary while pruning unnecessary node. In this paper, we propose new k-NN search algorithm based on density of object. With this method, we predict the radius to be expected to contain k-NN object using density of data set and search those objects within this radius and then adjust radius if failed. Experimental results show that this method outperforms the previous MINMAX distance method. This algorithm visit fewer disks than MINMAX method by the factor of maximum $22\%\;and\;average\;6\%.$

• Journal of the Korean Institute of Landscape Architecture
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• v.26 no.2
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• pp.38-53
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• 1998

This study quantified annual net carbon uptake by urban landscape trees and provided equations to estimate it for Ginkgo biloba, platanus occidentalis, Zelkova serrata and Acer palmatum, based on measurement of exchange rate for two years growing seasons from Sep., 1995 to Aug., 1997. The carbon uptake was significantly influenced by photosynthetic capacity, photon flux density and pruning. Ginkgo biloba showed the highest rate of net CO\sub 2\ uptake per unit leaf area and Acer palmatum did the lowest rate among those species. A tree shaded by adjacent building over the growing seasons showed net CO\sub2\ uptake per unit leaf area much lower than another tree of the same species less shaded. Annual net carbon uptake per tree was 19kg for Zelkova serrata, but only 1 kg for Ginkgo biloba and Platanus occidentalis with crown volume dwarfed from pruning. One Zekoval serrata tree annually offset carbon emission from consumption of about 32 liter of gasoline or 83 kWh of electricity. Strategies to improve CO\sub 2\ uptake by urban landscape trees include planting of species with high potosynthetic capacity, sunlight-guaranteed road and building layout for street trees, planting of shade-tolerant species in the north of buildings, and relocation of utility lines to underground and minimized pruning.

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.4
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• pp.59-70
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• 2003

Spatial data base system provides many query types and most of them are required frequent disk I/O and much CPU time. k-NN search is to find k-th closest object from the query point and up to now, several k-NN search methods have been proposed. Among these, MINMAX distance method has an aim not to access unnecessary node by adapting pruning technique. But this method accesses more disks than necessary while pruning unnecessary nodes. In this paper, we propose new k-NN search algorithm based on density of object. With this method, we predict the radius to be expected to contain k-NN objects using density of data set and search those objects within this radius and then adjust radius if failed. Experimental results show that this method outperforms the previous MINMAX distance method. This algorithm visit less disks than MINMAX method by the factor of maximum 22% and average 7%.

• ETRI Journal
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• v.46 no.3
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• pp.485-500
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• 2024

A hardware architecture is presented to decode (N, K) polar codes based on a low-density parity-check code-like decoding method. By applying suitable pruning techniques to the dense graph of the polar code, the decoder architectures are optimized using fewer check nodes (CN) and variable nodes (VN). Pipelining is introduced in the CN and VN architectures, reducing the critical path delay. Latency is reduced further by a fully parallelized, single-stage architecture compared with the log N stages in the conventional belief propagation (BP) decoder. The designed decoder for short-to-intermediate code lengths was implemented using the Virtex-7 field-programmable gate array (FPGA). It achieved a throughput of 2.44 Gbps, which is four times and 1.4 times higher than those of the fast-simplified successive cancellation and combinational decoders, respectively. The proposed decoder for the (1024, 512) polar code yielded a negligible bit error rate of 10^-4 at 2.7 E_b/N_o (dB). It converged faster than the BP decoding scheme on a dense parity-check matrix. Moreover, the proposed decoder is also implemented using the Xilinx ultra-scale FPGA and verified with the fifth generation new radio physical downlink control channel specification. The superior error-correcting performance and better hardware efficiency makes our decoder a suitable alternative to the successive cancellation list decoders used in 5G wireless communication.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.326-338
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• 2003

The objective of a neural network design and model selection is to construct an optimal network with a good generalization performance. However, training data include noises, and the number of training data is not sufficient, which results in the difference between the true probability distribution and the empirical one. The difference makes the teaming parameters to over-fit only to training data and to deviate from the true distribution of data, which is called the overfitting phenomenon. The overfilled neural network shows good approximations for the training data, but gives bad predictions to untrained new data. As the complexity of the neural network increases, this overfitting phenomenon also becomes more severe. In this paper, by taking statistical viewpoint, we proposed an integrative process for neural network design and model selection method in order to improve generalization performance. At first, by using the natural gradient learning with adaptive regularization, we try to obtain optimal parameters that are not overfilled to training data with fast convergence. By adopting the natural pruning to the obtained optimal parameters, we generate several candidates of network model with different sizes. Finally, we select an optimal model among candidate models based on the Bayesian Information Criteria. Through the computer simulation on benchmark problems, we confirm the generalization and structure optimization performance of the proposed integrative process of teaming and model selection.

• Journal of Environmental Science International
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• v.32 no.4
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• pp.233-242
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• 2023

The objective of this study was to develop a management strategy for the recovery of carbon storage capacity of abandoned coal mine forest rehabilitation area. For the purpose, the biomass and stand carbon storage over time after the forest rehabilitation by tree type for Betula platyphylla, Pinus densiflora, and Alnus hirsuta trees which are major tree species widely planted for the forest rehabilitation in the abandoned coal mine were calculated, and compared them with general forest. The carbon storage in abandoned coal mine forest rehabilitation areas was lower than that in general forests, and based on tree species, Pinus densiflora stored 48.9%, Alnus hirsuta 41.1%, and Betula platyphylla 27.0%. This low carbon storage is thought to be caused by poor growth because soil chemical properties, such as low TOC and total nitrogen content, in the soil of abandoned coal mine forest rehabilitation areas, were adverse to vegetation growth compared to those in general forests. DBH, stand biomass, and stand carbon storage tended to increase after forest rehabilitation over time, whereas stand density decreased. Stand' biomass and carbon storage increased as DBH and stand density increased, but there was a negative correlation between stand density and DBH. Therefore, after forest rehabilitation, growth status should be monitored, an appropriate growth space for trees should be maintained by thinning and pruning, and the soil chemical properties such as fertilization must be managed. It is expected that the carbon storage capacity the forest rehabilitation area could be restored to a level similar to that of general forests.

• Journal of KIISE:Databases
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• v.29 no.5
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• pp.367-380
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• 2002

Due to recent increase in applications requiring huge amount of data such as spatial data analysis and image analysis, clustering on large databases has been actively studied. In a hierarchical clustering method, a tree representing hierarchical decomposition of the database is first created, and then, used for efficient clustering. Existing hierarchical clustering methods mainly adopted the bottom-up approach, which creates a tree from the bottom to the topmost level of the hierarchy. These bottom-up methods require at least one scan over the entire database in order to build the tree and need to search most nodes of the tree since the clustering algorithm starts from the leaf level. In this paper, we propose a novel top-down hierarchical clustering method that uses multidimensional indexes that are already maintained in most database applications. Generally, multidimensional indexes have the clustering property storing similar objects in the same (or adjacent) data pares. Using this property we can find adjacent objects without calculating distances among them. We first formally define the cluster based on the density of objects. For the definition, we propose the concept of the region contrast partition based on the density of the region. To speed up the clustering algorithm, we use the branch-and-bound algorithm. We propose the bounds and formally prove their correctness. Experimental results show that the proposed method is at least as effective in quality of clustering as BIRCH, a bottom-up hierarchical clustering method, while reducing the number of page accesses by up to 26～187 times depending on the size of the database. As a result, we believe that the proposed method significantly improves the clustering performance in large databases and is practically usable in various database applications.

• Journal of the Korean Institute of Landscape Architecture
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• v.46 no.5
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• pp.82-92
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• 2018

The purpose of this study is to develop a model for the density management of planting sites and an additional model for new planting sites. In the Dongtan New Town of Hwaseong, there are buffer green spaces, with widths between 8m and 15m, between roads and apartment complexes. A total 38 survey plots were set to examine the planting patterns and the density of landscape trees. The Crown Overlapping Index (COI) was developed to assess the level of overcrowding as far as tree growth and development effectively. Pinus strobus recorded the most serious level of overcrowding growth and development. Its average density and average COI were very high at $0.3trees/m^2$ and 35.6%, respectively. There were many areas in which its COI was above 45%. The criteria for density management were set by standardizing the COI into three levels, which were above 45% (Type A), 30~45% (Type B), and under 30% (Type C). A model was proposed to manage poorly growing trees and to develop a model to select and manage trees of similar specification based on the planting patterns. The trees of density management areas were reviewed in terms of tree types and the ease of transplanting to establish an application system for the management plans according to the possibility of transplanting, thinning, and pruning. In new buffer green spaces, the planting density of Pinus strobus was lowered to $0.20{\sim}0.25trees/m^2$, with that of shrubs being reduced to $1.5{\sim}2.0trees/m^2$, leading to a planting design model to cover the lower parts in at least 30~40%.