• Journal of KIISE:Software and Applications
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• v.33 no.4
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• pp.402-410
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• 2006

This paper presents a method for feature based block motion estimation that uses multi -resolution image sequences to obtain the panoramic images in the continuous ultrasound image sequences. In the conventional block motion estimation method, the centers of motion estimation blocks are set at the predetermined and equally spaced locations. This requires the large blocks to include at least one feature, which inevitably requires long estimation time. In this paper, we propose an adaptive method which locates the center of the motion estimation blocks at the feature points. This make it possible to reduce the block size while keeping the motion estimation accuracy The Harris-Stephen corner detector is used to get the feature points. The comer points tend to group together, which cause the error in the global motion estimation. In order to distribute the feature points as evenly as Possible, the image is firstly divided into regular subregions, and a strongest corner point is selected as a feature in each subregion. The ultrasound Images contain speckle patterns and noise. In order to reduce the noise artifact and reduce the computational time, the proposed method use the multi-resolution image sequences. The first algorithm estimates the motion in the smoothed low resolution image, and the estimated motion is prolongated to the next higher resolution image. By this way the size of search region can be reduced in the higher resolution image. Experiments were performed on three types of ultrasound image sequences. These were shown that the proposed method reduces both the computational time (from 77ms to 44ms) and the displaced frame difference (from 66.02 to 58.08).

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.500-504
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• 2008

The objective of this paper is to evaluate the feasibility of using an optimization model as a effective way to search conjunctive operation scheme to meet two conditions; one is to minimize the electric cost for pumping and another is to meet the water demand for satisfying customers. The feasibility is confirmed as comparing the best combinations of pumps between multi-regional water supply networks from multiple sources which are obtained through an optimization modeling and EPAnet modeling. KModsim model, a network optimization model, was used to determine conjunctive operation scheme in the pipe system. KModsim, based on Lagrangian Relaxation algorithm, is useful for modeling network system and obtaining simultaneously pump combination and water allocation with given input option such as energy unit cost supplying from a source into a consumer, operating pumping combination. This study develops the procedure of determining optimal conjunctive operation scheme with using KModsim model. As a study region, the water supplying systems of the Geojae-city in the Geongsang Namdo Province was selected and investigated. The EPAnet hydraulic simulation result(Ryu et al, 2007, KSWW) gave input data for optimization model; energy unit price(won/$m^3$), water service available area etc.. It was assured that the combination of pump operation through optimum conjunctive operation is to be optimum scheme to obtain the best economic water allocation with comparison to the hydraulic simulation result such as electric cost and pump combination cases. The results obtained through the study are as follows. First, It was found that a well-allocated water supply scheme, the best combination of pump operation through optimum joint operation, promises to save the electric cost and satisfy all operational goals such as stability and revenues during the period. Second, an application of KModSim, a network model, gave the amount of water allocation from each source to a consumer with consideration of economic supply. Finally, in a service area available to supply through conjunctive operation of existing inter-regional water supply networks within short distance, a conjunctive operation is useful for determining each transmission pipeline's service area and maximizing the effectiveness of optimizations in pumping operation time.

• Journal of Korea Water Resources Association
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• v.47 no.4
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• pp.343-356
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• 2014

The SWMM (storm water management model) has been widely used in the world and is a watershed runoff simulation model used for a single event or a continuous simulation of runoff quantity and quality. However, there are many uncertain parameters in the watershed runoff continuous simulation module and the water quality module, which make it difficult to use the SWMM. The purpose of the study is to develop an automatic calibration module of the SWMM not only for watershed runoff continuous simulation, but also water quality simulation. The automatic calibration module was developed by linking the SWMM with the SCE-UA (shuffled complex evolution-University of Arizona) that is a global optimization algorithm. Estimation parameters of the SWMM were selected and search ranges of them were reasonably configured. The module was validated by calibration and verification of the watershed runoff continuous simulation model and the water quality model for the Donghyang Stage Station Basin. The calibration results for watershed runoff continuous simulation model were excellent and those for water quality simulation model were generally satisfactory. The module could be used in various studies and designs for watershed runoff and water quality analyses.

• Journal of the Korea Concrete Institute
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• v.26 no.3
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• pp.401-409
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• 2014

This research develops tools and strategies for optimizing RC column sections applied in tall buildings. Optimization parameters are concrete strength and section shape, the objective function for which is subject to several predefined constraints drawn from the original structural design. For this purpose, we developed new components for StrAuto, a parametric modeling and optimization tool for building structure. The components receive from external analysis solvers member strengths calculated from the original design model, and output optimized column sections satisfying the minimum cost. Using these components, optimized sections are firstly obtained for each predefined concrete strength applied to the whole floors in the project building. The obtained results for each concrete strength are comparatively examined to determine the fittest sections which will also result in the fittest vertical zoning for concrete strength. The main optimization scenario for this is to search for the vertical levels where the identical optimized sections coincide for the two different concrete strengths in concern, and select those levels for the boundaries where a concrete strength will be changed to another. The optimization process provided in this research is a product of an intensive development designed for a specific member in a specific project. Thus, the algorithm suggested takes on a microscopic and mathematical approach. However, the technique has a lot of potential that it can further be extensively developed and applied for future projects.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.9C
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• pp.868-875
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• 2003

In this paper, enhanced multiresolution motion estimation(MRME) using reduction of one-pixel shift in wavelet domain is proposed. Conventional multiresolution motion estimation using hierarchical relationship of wavelet coefficient has difficulty for accurate motion estimation due to shift-variant property by decimation process of the wavelet transform. Therefore, to overcome shift-variant property of wavelet coefficient, two level wavelet transform is performed. In order too reduce one-pixel shift on low band signal, S$_4$ band is interpolated by inserting average value. Secondly, one level wavelet transform is applied to the interpolated S$_4$ band. To estimate initial motion vector, block matching algorithm is applied to low band signal S$_{8}$. Multiresolution motion estimation is performed at the rest subbands in low level. According to the experimental results, proposed method showed 1-2dB improvement of PSNR performance at the same bit rate as well as subjective quality compared with the conventional multiresolution motion estimation(MRME) methods and full-search block matching in wavelet domain.

• Journal of the Korean earth science society
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• v.26 no.7
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• pp.683-690
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• 2005

As remote sensing imagery with high spatial resolution (e.g. pixel resolution of 1m or less) is used widely in the specific application domains, the requirements of advanced methods for this imagery are increasing. Among many applicable methods, the texture image analysis, which was characterized by the spatial distribution of the gray levels in a neighborhood, can be regarded as one useful method. In the texture image, we compared and analyzed different results according to various directions, kernel sizes, and parameter types for the GLCM algorithm. Then, we studied spatial feature characteristics within each result image. In addition, a template matching program which can search spatial patterns using template images selected from original and texture images was also embodied and applied. Probabilities were examined on the basis of the results. These results would anticipate effective applications for detecting and analyzing specific shaped geological or other complex features using high spatial resolution imagery.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.4 s.310
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• pp.88-102
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• 2006

In multimedia information retrieval, multimedia data are represented as vectors in high dimensional space. To search these vectors effectively, a variety of indexing methods have been proposed. However, the performance of these indexing methods degrades dramatically with increasing dimensionality, which is known as the dimensionality curse. To resolve the dimensionality curse, dimensionality reduction methods have been proposed. They map feature vectors in high dimensional space into the ones in low dimensional space before indexing the data. This paper proposes a method for dimensionality reduction based on a function approximating the Euclidean distance, which makes use of the norm and angle components of a vector. First, we identify the causes of the errors in angle estimation for approximating the Euclidean distance, and discuss basic directions to reduce those errors. Then, we propose a novel method for dimensionality reduction that composes a set of subvectors from a feature vector and maintains only the norm and the estimated angle for every subvector. The selection of a good reference vector is important for accurate estimation of the angle component. We present criteria for being a good reference vector, and propose a method that chooses a good reference vector by using Levenberg-Marquardt algorithm. Also, we define a novel distance function, and formally prove that the distance function lower-bounds the Euclidean distance. This implies that our approach does not incur any false dismissals in reducing the dimensionality effectively. Finally, we verify the superiority of the proposed method via performance evaluation with extensive experiments.

• The Journal of Bigdata
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• v.4 no.2
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• pp.23-34
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• 2019

Recently, the crime that utilizes the digital platform is continuously increasing. About 140,000 cases occurred in 2015 and about 150,000 cases occurred in 2016. Therefore, it is considered that there is a limit handling those online crimes by old-fashioned investigation techniques. Investigators' manual online search and cognitive investigation methods those are broadly used today are not enough to proactively cope with rapid changing civil crimes. In addition, the characteristics of the content that is posted to unspecified users of social media makes investigations more difficult. This study suggests the site-based collection and the Open API among the content web collection methods considering the characteristics of the online media where the infringement crimes occur. Since illegal content is published and deleted quickly, and new words and alterations are generated quickly and variously, it is difficult to recognize them quickly by dictionary-based morphological analysis registered manually. In order to solve this problem, we propose a tokenizing method in the existing dictionary-based morphological analysis through WPM (Word Piece Model), which is a data preprocessing method for quick recognizing and responding to illegal contents posting online infringement crimes. In the analysis of data, the optimal precision is verified through the Vote-based ensemble method by utilizing a classification learning model based on supervised learning for the investigation of illegal contents. This study utilizes a sorting algorithm model centering on illegal multilevel business cases to proactively recognize crimes invading the public economy, and presents an empirical study to effectively deal with social data collection and content investigation.

• Spatial Information Research
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• v.18 no.4
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• pp.129-140
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• 2010

Large portal sites such as Google, NAVER provide Various services based on the map. Thus, interest and demand of users who want to obtain the region-related information has been increased. And services that combine the regional information with the map are provided currently at the large portal sites. However, the existing services of large portal sites do not provide enough detailed information and are inconvenient because acquisition process of related information is repeated. Therefore, the system that enables users to obtain detailed information related on the specific region synthetically and easily is needed. In this paper, we propose a system model using map interface and knowledge-processing in order to build the system that is useful for acquiring regional information. The model consists of 3-Layers: 'Regional Information Web-Documents Layer', 'Unique Regional Information Layer', and "Map-Interface Layer'. The Integrated Region~Related Information Searching System based on the model is implemented through the following 4-steps: (1) extracting the keywords that represent specific region (2) collecting the related web pages (3) extracting a set of related keywords and computing an association between the keywords (4) implementing a user interface. We verified validity on the model we proposed. knowledge-processing algorithm using affinity matrix, and UI that help users conveniently search by applying the system to region of the Goyang City. This system integrates regional information existing merely individual 'information' and provides users the 'knowledge' that is newly produced and organized. Users can obtain various detailed regional information and easily get related information through this system.

• Korean Journal of Clinical Laboratory Science
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• v.52 no.3
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• pp.229-236
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• 2020

Sixty-five molds isolated from clinical specimens were included in this study. All the isolates were molds that could be identified morphologically, strains that are difficult to identify because of morphological similarities, and strains that require species-level identification. PCR and direct sequencing were performed to target the internal transcribed spacer (ITS) region, the D1/D2 region, and the β-tubulin gene. Comparative sequence analysis using the GenBank database was performed using the basic local alignment search tool (BLAST) algorithm. The fungi identified morphologically to the genus level were 67%. Sequencing analysis was performed on 62 genera and species level of the 65 strains. Discrepancies were 14 (21.5%) of the 65 strains between the results of phenotypic and molecular identification. B. dermatitidis, T. marneffei, and G. argillacea were identified for the first time in Korea using the DNA sequencing method. Morphological identification is a very useful method in terms of the reporting time and costs in cases of frequently isolated and rapid growth, such as Aspergillus. When molecular methods are employed, the cost and clinical significance should be considered. On the other hand, the molecular identification of molds can provide fast and accurate results.