• The KIPS Transactions:PartD
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• v.11D no.1
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• pp.23-30
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• 2004

In order to approximate the spatial query result size we partition the input rectangles into subsets and estimate the query result size based on the partitioned spatial area. In this paper we examine query result size estimation in skewed data. We examine the existing spatial partitioning techniques such as equi-area and equi-count partitioning, which are analogous to the equi-width and equi-height histograms used in relational databases, and examine the other partitioning techniques based on spatial indexing. In this paper we propose a new spatial partitioning technique based on the Hilbert space filling curve. We present a detailed experimental evaluation comparing the proposed technique and the existing techniques using synthetic as well as real-life datasets. The experiments showed that the proposed partitioning technique based on the Hilbert space filling curve achieves better query result size estimation than the existing techniques for space query size, bucket numbers, skewed data, and spatial data size.

• Journal of Korea Multimedia Society
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• v.13 no.5
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• pp.763-770
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• 2010

This paper proposes an algorithm to enhance the performance of the spatial partitioning method for a flocking behavior. One of the characteristics in a flocking behavior is that two agents may share many common neighbors if they are spatially close to each other. This paper improves the spatial partitioning method by applying this characteristic. While the conventional spatial partitioning method computes the k-nearest neighbors of an agent one by one, the proposed method computes simultaneously the k-nearest neighbors of agents if they are spatially close to each other. The proposed algorithm was implemented and its performance was experimentally compared with the original spatial partitioning method. The results of the comparison showed that the proposed algorithm outperformed the original method by about 33% in average.

• Journal of Korea Game Society
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• v.13 no.4
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• pp.25-34
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• 2013

Spatial partitioning trees are needed for processing collision detections efficiently. In order to select split planes for spatial partitioning trees, the tree balance and the number of polygons overlapped with the split plane should be considered. In this paper, the heuristic algorithm controlling weight values of tree build criteria is proposed for spatial partitioning trees of 3D games. As the weight values are changed, tree build time, T-junction elimination time which can cause visual artifacts in splitting polygons overlapped with the split plane, rendering speed (frame per second: FPS) according to tree balance are analysed under 3D game simulations.

• Journal of Korea Game Society
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• v.9 no.2
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• pp.115-123
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• 2009

This paper proposes an algorithm to improve the performance of the spatial partitioning method for flocking behaviors. The core concept is to improve the performance by using the fact that even if a moving entity, boid in flock continuously changes its direction and position, its k-nearest neighbors, kNN to effect on decision of the next direction is not changed frequently. From the previous kNN, the method to check whether new kNN is changed or not is proposed in this paper and then the correctness of the proposed method is proved by two theorems. The proposed algorithm was implemented and its performance was compared with the conventional spatial partitioning method. The results of the comparison show that the proposed algorithm outperforms the conventional one by about 30% with respect to the number of frames per a second.

• ETRI Journal
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• v.34 no.6
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• pp.900-910
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• 2012

In this paper, a novel trellis-coded spatial modulation (TCSM) design method is presented and analyzed. Inspired by the key idea of trellis-coded modulation (TCM), the detailed analysis is firstly provided on the unequal error protection performance of spatial modulation constellation. Subsequently, the Ungerboeck set partitioning rule is proposed and applied to develop a general method to design the novel TCSM schemes. Different from the conventional TCSM approaches, the novel one based on the Ungerboeck set partitioning rule has similar properties as the classic TCM, which has simple but effective code design criteria. Moreover, the novel designed schemes are robust and adaptive to the generalized Rician fading channels, which outperform the traditional TCSM ones. For examples, the novel 4-, 8-, and 16-state TCSM schemes are constructed by employing different transmit antennas and different modulation schemes in different channel conditions. Simulation results clearly demonstrate the advantages of the novel TCSM schemes over the conventional ones.

• KIEAE Journal
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• v.11 no.6
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• pp.95-100
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• 2011

Although Space Syntax is a well-known technique for spatial analysis, debates have taken place among some researchers because the Space Syntax discards geometric information as both shapes and sizes of spaces, and hence may cause some inconsistencies. Therefore, this study aims at developing methods to recognize and manage spatial shapes for more precise space syntax analysis. To reach this goal, this study employed both a graph theory and binary spatial partitioning (BSP) tree to recognize and manage spatial information. As a result, spatial shapes and sizes could be recognized by checking loops in graph converted from spatial shapes of built environment. Each spatial shape could be managed sequentially by BSP tree with hierarchical structure. Through such recognition and management processes, convex maps composed of the fattest and fewest convex spaces could be drawn. In conclusion, we hope that the methods developed here will be useful for urban planning to find appropriate purposes of spaces to satisfy the sustainability of built environment on the basis of the spatial and social relationships in urban spaces.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5244-5259
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• 2019

With the continuous development of LBS (Location Based Service) applications, privacy protection has become an urgent problem to be solved. Differential privacy technology is based on strict mathematical theory that provides strong privacy guarantees where it supposes that the attacker has the worst-case background knowledge and that knowledge has been applied to different research directions such as data query, release, and mining. The difficulty of this research is how to ensure data availability while protecting privacy. Spatial multidimensional data are usually released by partitioning the domain into disjointed subsets, then generating a hierarchical index. The traditional data-dependent partition methods need to allocate a part of the privacy budgets for the partitioning process and split the budget among all the steps, which is inefficient. To address such issues, a novel two-step partition algorithm is proposed. First, we partition the original dataset into fixed grids, inject noise and synthesize a dataset according to the noisy count. Second, we perform IH-Tree (Improved H-Tree) partition on the synthetic dataset and use the resulting partition keys to split the original dataset. The algorithm can save the privacy budget allocated to the partitioning process and obtain a more accurate release. The algorithm has been tested on three real-world datasets and compares the accuracy with the state-of-the-art algorithms. The experimental results show that the relative errors of the range query are considerably reduced, especially on the large scale dataset.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.367-374
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• 2010

As an alternative to the k-means clustering the k-spatial medians clustering has many good points because of advantages of spatial median. However, it has not been used a lot since it needs heavy computation. If the number of objects and the number of variables are large the computation time problem is getting serious. In this study we propose fast algorithm for the k-spatial medians clustering. Practical applicability of the algorithm is shown with some numerical studies.

• Journal of Korea Spatial Information System Society
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• v.12 no.1
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• pp.37-48
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• 2010

In this paper, we analyze the shortcomings of the previous declustering methods, which are based on grid-like partitioning and a mapping function from a cell to a disk number, for high-dimensional space and propose a solution. The problems arise from the fact that the number of splitting is small(for the most part, binary-partitioning is sufficient), and the side length of a range query whose selectivity is small is quite large. To solve this problem, we propose a mathematical model to estimate the performance of a grid-like partitioning method. With the proposed estimation model, we can choose a good grid-like partitioning method among the possible schemes and this results in overall improvement in declustering performance. Several experimental results show that we can improve the performance of a previous declustering method up to 2.7 times.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.961-968
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• 2018

Recently, researches on automatic recognition of human activities have been actively carried out with the emergence of various intelligent systems. Since a large amount of visual data can be secured through Closed Circuit Television, it is required to recognize human behavior in a dynamic situation rather than a static situation. In this paper, we propose new intelligent human activity recognition model using the trajectory information extracted from the video sequence. The proposed model consists of three steps: segmentation and partitioning of trajectory step, feature extraction step, and behavioral learning step. First, the entire trajectory is fuzzy partitioned according to the motion characteristics, and then temporal features and spatial features are extracted. Using the extracted features, four pedestrian behaviors were modeled by decision tree learning algorithm and performance evaluation was performed. The experiments in this paper were conducted using Caviar data sets. Experimental results show that trajectory provides good activity recognition accuracy by extracting instantaneous property and distinctive regional property.