• Journal of Korean Society for Atmospheric Environment
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• v.26 no.5
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• pp.517-532
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• 2010

After establishment of Banwol industrial complex in 1987, Ansan city becomes the largest industrial sector development in Gyeonggi-do, Korea. As the population and industrial activity grow over this region, toxic air pollutants, particularly POPs (Persistent Organic Pollutants) from various emission sources have been major public concerns. Air samples for POPs monitoring were collected at the industrial sites ($A_2$), residential sites ($B_1$, $B_2$), commercial site (C), and rural/remote site (D) of the area of Ansan during 2008 with a prolonged industrial sampling site $A_1$ from 2001 to 2008. All samples were analysed for 2,3,7,8 substituted-polychlorinated dibenzo-p-dioxin and dibenzofurans (PCDD/Fs) and dioxin like polychlorinatd diphenyls (dl-PCBs). In site $A_1$, a steady decline of their concentrations from 2003 to 2008 was observed due to the reinforced emission guideline from waste incinerators. The average concentration of the PCDD/Fs and dl-PCBs ranged between 0.118 pg-TEQ/$m^3$ (rural/remote site D) and 0.532 pg-TEQ/$m^3$ (industrial area $A_2$). These level were generally consistent with previous studies in Gyeonggi-do, while higher than other places. Most of PCDD/Fs congener were partitioned into particle phase, whereas dl-PCBs were partitioned into gas phase. The logarithm of gas-particle partition coefficient $K_P$ of dl-PCBs and PCDD/Fs were well correlated with sub-cooled liquid vapor pressure $P_L$. The slope $m_T$ of log $K_P$ versus log $P_L$ for PCDD/Fs (-1.22) and dl-PCBs (-1.02) in industrial area ($A_2$) were high compared to other residential/commercial area. It suggests that this area was likely influenced by the direct emission source of PCDD/Fs and dl-PCBs. To simulate the partition of PCDD/Fs and dl-PCBs between gas and particle phase, Junge-Pankow model ($P_L$-base) and $K_{oa}$ model were applied. It was found that J-P model was more suitable than the $K_{oa}$ model in this study.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.1
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• pp.33-40
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• 2002

In this paper, we proposed a lossless compression algorithm of medical images which is essential technique in picture archive and communication system. Mammographic image and magnetic resonance image in among medical images used in this study, proposed a region growing segmentation algorithm for compression of these images. A proposed algorithm was partition by three sub region which error image, discontinuity index map, high order bit data from original image. And generated discontinuity index image data and error image which apply to a region growing algorithm are compressed using JBIG(Joint Bi-level Image experts Group) algorithm that is international hi-level image compression standard and proper image compression technique of gray code digital Images. The proposed lossless compression method resulted in, on the average, lossless compression to about 73.14% with a database of high-resolution digital mammography images. In comparison with direct coding by JBIG, JPEG, and Lempel-Ziv coding methods, the proposed method performed better by 3.7%, 7.9% and 23.6% on the database used.

• 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.

• Proceedings of the KSRS Conference
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• v.2
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• pp.610-614
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• 2006

In this paper the advanced method of geodesic active contours was developed for the task of building detection from aerial and satellite images. Automatic extraction of man-made structures including buildings, building blocks or roads from remote sensing data is useful for land use mapping, scene understanding, robotic navigation, image retrieval, surveillance, emergency management procedures, cadastral etc. A level set method based on a region-driven segmentation model was implemented with which building boundaries were detected, through this curve propagation technique. The essence of this approach is to optimize the position and the geometric form of the curve by measuring information along that curve, and within the regions that compose the image partition. To this end, one can consider uniform intensities inside objects and the background. Thus, given an initial position of the curve, one can determine global, region-driven functions and provide a statistical description of the inside and outside object area. The calculus of variations and a gradient descent method was used to optimize the variational functional by an iterative steady state process. Experimental results demonstrate the potential of the proposed processing scheme.

• Convergence Security Journal
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• v.6 no.2
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• pp.53-60
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• 2006

In the applications such as CAD or image processing, a variety of geometric objects are manipulated. A polygon in which all the edges are parallel to x- or y-axis is decomposed into simple rectangles for efficient handling. But, depending on the partitioning algorithms, the same region can be decomposed into a completely different set of rectangles in the number, size and shape of rectangles. So, it is necessary an algorithm that compares two sets of rectangles extracted from two scenes such as CAD or image to see if they represent the same region. This paper proposes an efficient algorithm that compares two sets of rectangles. The proposed algorithm is not only simpler than the algorithm based on sweeping method, but also reduces the number $O(n^2)$ of overlapped rectangles from the algorithm based on a balanced binary tree to O(nlogn).

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.368-374
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• 2010

Image segmentation is the basic step in the field of the image processing for pattern recognition, environment recognition, and context analysis. The Otsu's automatic threshold selection, which determines the optimal threshold value to maximize the between class scatter using the distribution information of the normalized histogram of a image, is the famous method among the various image segmentation methods. For the automatic threshold selection proposed by Otsu, it is difficult to determine the optimal threshold value by considering the sub-region characteristic of the image because the Otsu's algorithm analyzes the global histogram of a image. In this paper, to alleviate this difficulty of Otsu's image segmentation algorithm and to improve image segmentation capability, the original image is divided into several sub-images by using context fuzzy c-means algorithm. The proposed fuzzy Otsu threshold algorithm is applied to the divided sub-images and the several threshold values are obtained.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.3
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• pp.305-323
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• 2010

This paper proposes an efficient and yet powerful local descriptor called phase-space partition based descriptor (PPD). This descriptor is designed for the mobile image matching and retrieval. PPD, which is inspired from SIFT, also encodes the salient aspects of the image gradient in the neighborhood around an interest point. However, without employing SIFT's smoothed gradient orientation histogram, we apply the region based gradient statistics in phase space to the construction of a feature representation, which allows to reduce much computation requirements. The feature matching experiments demonstrate that PPD achieves favorable performance close to that of SIFT and faster building and matching. We also present results showing that the use of PPD descriptors in a mobile image retrieval application results in a comparable performance to SIFT.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.14 no.1
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• pp.57-66
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• 2010

In this paper, we develop the Automatic Number Plate Recognition (ANPR) System. ANPR is generally composed of the following four steps: i) The acquisition of the image; ii) The extraction of the region of the number plate; iii) The partition of the number and iv) The recognition. The second and third steps incorporate image processing technique. We propose to resolve this by using Partial Differential Equation(PDE) based segmentation method. This method is computationally efficient and robust. Results indicate that our methods are capable to recognize the plate number on difficult situations.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.04a
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• pp.253-257
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• 2000

the objective of this study is to design and evaluate a methodology for diagnosing the appendicitis in a fuzzy neural network that integrates the partition of input space by fuzzy entropy and the generation of fuzzy control rules and learning algorithm. In particular the diagnosis of appendicitis depends on the rule of thumb of the experts such that it associates with the region, the characteristics, the degree of the ache and the potential symptoms. In this scheme the basic idea is to realize the fuzzy rle base and the process of reasoning by neural network and to make the corresponding parameters of the fuzzy control rules be adapted by back propagation learning rule. To eliminate the number of the parameters of the rules, the output of the consequences of the control rules is expressed by the network's connection weights. As a result we obtain a method for reducing the system's complexities. Through computer simulations the effectiveness of the proposed strategy is verified for the diagnosis of appendicitis.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.204-208
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• 1990

The digital instrumentation and control system for the large scale system like the power plant must have the form of the heirachical structure. Because most large scale system have many control and process signals and it is distributed in the vade region, it is necessary to partition them into several subsystems. Therefore, the role of SCS(Supervisory Control System) having the functions of controlling and monitoring for the status of subsystems is very important. In this paper, new SCS for the effective control of the large scale system is proposed.