• The KIPS Transactions:PartA
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• v.10A no.6
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• pp.665-670
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• 2003

In the applications like automatic masks generation for semiconductor production, a drawing consists of lots of polygons that are partitioned into trapezoids. The addition/deletion of a polygon to/from the drawing is performed through geometric operations such as insertion, deletion, and search of trapezoids. Depending on partitioning algorithm being used, a polygon can be partitioned differently in terms of shape, size, and so on. So, It's necessary to invent some comparison algorithm of sets of trapezoids in which each set represents interested parts of a drawing. This comparison algorithm, for example, may be used to verify a software program handling geometric objects consisted of trapezoids. In this paper, given k sets of trapezoids in which each set forms the regions of interest of each drawing, we present how to compare the k sets to see if all k sets represent the same geometric scene. When each input set has the same number n of trapezoids, the algorithm proposed has O(2$^{k-2}$ $n^2$(log n＋k)) time complexity. It is also shown that the algorithm suggested has the same time complexity O( $n^2$ log n) as the sweeping-based algorithm when the number k(<< n) of input sets is small. Furthermore, the proposed algorithm can be kn times faster than the sweeping-based algorithm when all the trapezoids in the k input sets are almost the same.

• The KIPS Transactions:PartB
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• v.17B no.5
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• pp.363-374
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• 2010

In this paper, we propose the method for extracting Homogeneity Threshold($H_T$) and for segmenting homogeneous regions by USRG(Unseeded Region Growing) with $H_T$. The $H_T$ is a criterion to distinguish homogeneity in neighbor pixels and is computed automatically from the original image by proposed method. Theoretical background for proposed method is based on the Otsu's single level threshold method. The method is used to divide a small local part of original image int o two classes and the sum($\sigma_c$) of standard deviations for the classes to satisfy special conditions for distinguishing as different regions from each other is used to compute $H_T$. To find validity for proposed method, we compare the original image with the image that is regenerated with only the segmented homogeneous regions and show up the fact that the difference between two images is not exist visually and also present the steps to regenerate the image in order the size of segmented homogeneous regions and in order the intensity that includes pixels. Also, we show up the validity of proposed method with various results that is segmented using the homogeneity thresholds($H^*_T$) that is added a coefficient ${\alpha}$ for adjusting scope of $H_T$. We expect that the proposed method can be applied in various fields such as visualization and animation of natural image, anatomy and biology and so on.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.11
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• pp.757-764
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• 2013

The estimation of maintenance cost of information system so far has been conducted centered on the ordering body, so the problem of provider's having to cover the cost due to small cost compared to the amount of work is not solved. This study is a base study for estimating the maintenance cost of information system centered on provider, and it deduces cost items of maintenance and suggests adjustment factors for adjusting the gap between the ordering body and provider regarding the maintenance cost. In order to deduce the cost items of maintenance, this study adds the activities of the provider for maintenance to the base study of cost factors regarding the existing maintenance activity, divides, and classifies them into the fixed cost and variable cost. In order to adjust the gap between the ordering body and provider regarding the maintenance cost, this study found the adjustment factors such as the code, utility, and components created by the automatic tool that was not included when estimating the maintenance cost centered on the ordering body. After examining and analyzing K Company's data of maintenance performance for three years, it confirmed that the gap regarding the adjustment factors was about 13% in case of K Company.

• Journal of KIISE
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• v.43 no.3
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• pp.370-379
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• 2016

Recently personal media were produced in a variety of ways as a lot of smart devices have been spread and services using these data have been desired. Therefore, research has been actively conducted for the media analysis and recognition technology and we can recognize the meaningful object from the media. The system using the media ontology has the disadvantage that can't classify the media appearing in the video because of the use of a video title, tags, and script information. In this paper, we propose a system to automatically classify video using the objects shown in the media data. To do this, we use a description logic-based reasoning and a rule-based inference for event processing which may vary in order. Description logic-based reasoning system proposed in this paper represents the relation of the objects in the media as activity ontology. We describe how to another rule-based reasoning system defines an event according to the order of the inference activity and order based reasoning system automatically classify the appropriate event to the category. To evaluate the efficiency of the proposed approach, we conducted an experiment using the media data classified as a valid category by the analysis of the Youtube video.

• Korean Journal of Remote Sensing
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• v.27 no.1
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• pp.33-41
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• 2011

In the information communication technology, it is world-widely apparent that trend movement from internet web to smartphone app by users demand and developers environment. So it needs kinds of appropriate technological responses from geo-spatial domain regarding this trend. However, most cases in the smartphone app are the map service and location recognition service, and uses of geo-spatial contents are somewhat on the limited level or on the prototype developing stage. In this study, app for extraction of corner point features using geo-spatial imagery and their linkage to database system are developed. Corner extraction is based on Harris algorithm, and all processing modules in database server, application server, and client interface composing app are designed and implemented based on open source. Extracted corner points are applied LOD(Level of Details) process to optimize on display panel. Additional useful function is provided that geo-spatial imagery can be superimposed with the digital map in the same area. It is expected that this app can be utilized to automatic establishment of POI (Point of Interests) or point-based land change detection purposes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.75-84
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• 2018

Increasing the operation rate of components and stabilizing the operation through timely management of the core parts are crucial for improving the efficiency of the railroad maintenance industry. The demand for diagnosis technology to assess the condition of rolling stock components, which employs history management and automated big data analysis, has increased to satisfy both aspects of increasing reliability and reducing the maintenance cost of the core components to cope with the trend of rapid maintenance. This study developed a big data platform-based system to manage the rolling stock component condition to acquire, process, and analyze the big data generated at onboard and wayside devices of railroad cars in real time. The system can monitor the conditions of the railroad car component and system resources in real time. The study also proposed a machine learning technique that enabled the distributed and parallel processing of the acquired big data and automatic component fault diagnosis. The test, which used the virtual instance generation system of the Amazon Web Service, proved that the algorithm applying the distributed and parallel technology decreased the runtime and confirmed the fault diagnosis model utilizing the random forest machine learning for predicting the condition of the bearing and wheel parts with 83% accuracy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.672-679
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• 2018

This paper describes a methodology for shape design using an optimal design system, whereas generally a three dimensional analysis is required for such designs. An automatic finite element mesh generation technique, which is based on fuzzy knowledge processing and computational geometry techniques, is incorporated into the system, together with a commercial FE analysis code and a commercial solid modeler. Also, with the aid of multilayer neural networks, the present system allows us to automatically obtain a design window, in which a number of satisfactory design solutions exist in a multi-dimensional design parameter space. The developed optimal design system is successfully applied to evaluate the structures that are used. This study used a stress gauge to measure the maximum stress affecting the parts of the side housing bracket which are most vulnerable to cracking. Thereafter, we used a tool to interpret the maximum stress value, while maintaining the same stress as that exerted on the spot. Furthermore, a stress analysis was performed with the typical shape maintained intact, SM490 used for the material and the minimizing weight safety coefficient set to 3, while keeping the maximum stress the same as or smaller than the allowable stress. In this paper, a side housing bracket with a comparably simple structure for 36 tons was optimized, however if the method developed in this study were applied to side housing brackets of different classes (tons), their quality would be greatly improved.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.3
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• pp.187-194
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• 2012

Prostate cancer is one of the most frequent cancers in men and is a major cause of mortality in the most of countries. In many diagnostic and treatment procedures for prostate disease, transrectal ultrasound(TRUS) images are being used because the cost is low. But, accurate detection of prostate boundaries is a challenging and difficult task due to weak prostate boundaries, speckle noises and the short range of gray levels. This paper proposes a method for automatic prostate segmentation in TRUS images using its average shape model and invariant features. This approach consists of 4 steps. First, it detects the probe position and the two straight lines connected to the probe using edge distribution. Next, it acquires 3 prostate patches which are in the middle of average model. The patches will be used to compare the features of prostate and nonprostate. Next, it compares and classifies which blocks are similar to 3 representative patches. Last, the boundaries from prior classification and the rough boundaries from first step are used to determine the segmentation. A number of experiments are conducted to validate this method and results showed that this new approach extracted the prostate boundary with less than 7.78% relative to boundary provided manually by experts.

• The KIPS Transactions:PartB
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• v.19B no.1
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• pp.1-8
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• 2012

Mosaic is to make a big image by gathering lots of small materials having various colors. With advance of digital imaging techniques, photomosaic techniques using photos are widely used. In this paper, we presents an automatic photomosaic algorithm based on adaptive tiling and block matching. The proposed algorithm is composed of two processes: photo database generation and photomosaic generation. Photo database is a set of photos (or tiles) used for mosaic, where a tile is divided into $4{\times}4$ regions and the average RGB value of each region is the feature of the tile. Photomosaic generation is composed of 4 steps: feature extraction, adaptive tiling, block matching, and intensity adjustment. In feature extraction, the feature of each block is calculated after the image is splitted into the preset size of blocks. In adaptive tiling, the blocks having similar similarities are merged. Then, the blocks are compared with tiles in photo database by comparing euclidean distance as a similarity measure in block matching. Finally, in intensity adjustment, the intensity of the matched tile is replaced as that of the block to increase the similarity between the tile and the block. Also, a tile redundancy minimization scheme of adjacent blocks is applied to enhance the quality of mosaic photos. In comparison with Andrea mosaic software, the proposed algorithm outperforms in quantitative and qualitative analysis.

• The KIPS Transactions:PartA
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• v.16A no.3
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• pp.209-216
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• 2009

The ubiquitous smart home is the home of the future that takes advantage of context information from the human and the home environment and provides an automatic home service for the human. Human location and motion are the most important contexts in the ubiquitous smart home. We present a real-time human tracker that predicts human location and motion for the ubiquitous smart home. We used four network cameras for real-time human tracking. This paper explains the real-time human tracker's architecture, and presents an algorithm with the details of two functions (prediction of human location and motion) in the real-time human tracker. The human location uses three kinds of background images (IMAGE1: empty room image, IMAGE2: image with furniture and home appliances in the home, IMAGE3: image with IMAGE2 and the human). The real-time human tracker decides whether the human is included with which furniture (or home appliance) through an analysis of three images, and predicts human motion using a support vector machine. A performance experiment of the human's location, which uses three images, took an average of 0.037 seconds. The SVM's feature of human's motion recognition is decided from pixel number by array line of the moving object. We evaluated each motion 1000 times. The average accuracy of all the motions was found to be 86.5%.