• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.28 no.2
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• pp.81-88
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• 2016

There are many types of national and international standards for low temperature tests depending on the products. This study conducted CFD analysis and estimation of the energy efficiency of the chamber both with and without a test object by considering variations of COP and specific volume according to temperature. The supply air was located in the upper area to compare the cooling performance for each location using various outlets in mixed ventilated conditions. For cases without the test object, the air temperature of the upper supply and center extract on the opposite side type chamber cooled faster than other areas by 4.3~29.8%. However, for cases with the test object, the object temperature of the upper supply and lower extract type chamber cooled faster than the other areas by 7.2~31.5%. The cooling efficiency of the air inside the chamber and the test object did not show the same pattern, which indicates the need to consider the cooling performance by not only the air but also the test object in the cryogenic chamber design for testing.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.7
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• pp.956-962
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• 2018

As the study of the object feature detection from image, we explain methods to identify the species of the tree in forest using the picture taken from dron. Generally there are three kinds of methods, which are GLCM (Gray Level Co-occurrence Matrix) and Gabor filters, in order to extract the object features. We proposed the object extraction method using the statistical properties of trees in this research because of the similarity of the leaves. After we extract the sample images from the original images, we detect the objects using cross correlation techniques between the original image and sample images. Through this experiment, we realized the mean value and standard deviation of the sample images is very important factor to identify the object. The analysis of the color component of the RGB model and HSV model is also used to identify the object.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.1
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• pp.35-41
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• 2012

This paper proposes a moving object detection algorithm for active camera system that can be applied to mobile robot and intelligent surveillance system. Most of moving object detection algorithms based on a stationary camera system. These algorithms used fixed surveillance system that does not consider the motion of the background or robot tracking system that track pre-learned object. Unlike the stationary camera system, the active camera system has a problem that is difficult to extract the moving object due to the error occurred by the movement of camera. In order to overcome this problem, the motion of the camera was compensated by using SURF and Pseudo Perspective model, and then the moving object is extracted efficiently using stochastic Label Cluster transport model. This method is possible to detect moving object because that minimizes effect of the background movement. Our approach proves robust and effective in terms of moving object detection in active camera system.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.535-540
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• 2009

Recently, autonomous robots which can achieve the complex tasks have been required with the advance of robotics. Advanced robot vision for recognition is necessary for the realization of such robots. In this paper, we propose a method to recognize an object in the actual environment. We assume that a 3D-object model used in our proposal method is the voxel data. Its inside is full up and its surface has color information. We also define the word "recognition" as the estimation of a target object's condition. This condition means the posture and the position of a target object in the actual environment. The proposal method consists of three steps. In Step 1, we extract features from the 3D-object model. In Step 2, we estimate the position of the target object. At last, we estimate the posture of the target object in Step 3. And we experiment in the actual environment. We also confirm the performance of our proposal method from results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.11
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• pp.2321-2326
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• 2011

Cervical vertebrae are a complex structure and an important part of human body connecting the head and the trunk. In this paper, we propose a method to extract sternocleidomastoid muscle from ultrasonography images of cervical vertabrae automatically. In our method, Region of Interests(ROI) is extracted first from an ultrasonography image after removing unnecessary auxiliary information such as metrics. Then we apply Ends-in search stretching algorithm in order to enhance the contrast of brightness. Average binarization is then applied to those pixels which its brightness is sufficiently large. The noise part is removed by image processing algorithms. After extracting fascia encloses sternocleidomastoid muscle, target muscle object is extracted using the location information of fascia according to the number of objects in the fascia. When only one object is to be extracted, we search downward first to extract the target muscle area and then search from right to left to extract the area and merge them. If there are two target objects, we extract first from the upper-bound of higher object to the lower-bound of lower object and then remove the fascia of the target object area. Smearing technique is used to restore possible loss of the fat area in the process. The thickness of sternocleidomastoid muscle is then calculated as the maximum thickness of those extracted objects. In this experiment with 30 real world ultrasonography images, the proposed method verified its efficacy and accuracy by health professionals.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.161.5-161
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• 2001

Robots are required to assist our activities in daily life. In this paper, we focus on arm movement to catch moving object as one of important tasks frequently performed by human. We propose an algorithm which enables a robot to perform human-like arm motion to catch a moving object. First we analyze human hand trajectories and velocity profiles to catch an object. From the experimental results, we extract some characteristics in the process of approaching and following a moving object and confirm that these are necessary to realize human-like motion. We then adopt an instantaneous optimal control method which evaluates the error and energy cost at each sampling step, and design two time-varying weight matrices to introduce human characteristic into robot motion. The matrix concerning the error is defined as a time-increasing ...

• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.4
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• pp.115-121
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• 2016

Recently, it has become an important problem to extract semantic objects from videos, which are useful for improving the performance of video compression and video retrieval. In this thesis, an automatic extraction method of moving objects of interest in video is suggested. We define that an moving object of interest should be relatively large in a frame image and should occur frequently in a scene. The moving object of interest should have different motion from camera motion. Moving object of interest are determined through spatial continuity by the AMOS method and moving histogram. Through experiments with diverse scenes, we found that the proposed method extracted almost all of the objects of interest selected by the user but its precision was 69% because of over-extraction.

• Journal of the Korea Society of Computer and Information
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• v.20 no.7
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• pp.17-24
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• 2015

In this paper, we propose a method to solve ghosting problem. To generate adaptive background, using an exponentially decreasing number of frames, may improve object detection performance. To extract moving objects from the background by using a differential image, detection error may be caused by object rotations or environmental changes. A ghosting problem can be issue-driven when there are outdoor environmental changes and moving objects. We studied that a differential image by adaptive background may reduce the ghosting problem. In experimental results, we test that our method can solve the ghosting problem.

• The Transactions of the Korea Information Processing Society
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• v.7 no.3
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• pp.818-830
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• 2000

Automation tools for program analysis are needed in order to program understand and maintain, extract the characteristics of object-oriented program such as class name, member function and data member. In this paper, we carried out design and implementation of the automation tool for effective maintenance of object-oriented software. Being based on Reverse Engineering, this approach extracts class relationship information from C++ source code and generates object-oriented model of class diagram using UML as the standard object-oriented methodology. Therefore, this paper provides developers visualized including class information, definitions of classes, inheritance relationships, set relationships, and simple reference relationships. Finally in this paper, we propose a method that construct class relationship information to table in analysis state and make form of table construction to link form so tat developers can perform understanding and maintaining program efficiently. And this method enable to restructure and reuse in object-oriented model.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1404-1407
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• 2013

Mobile phone GPS positioning system is a function we often use, which can make user locate his own position all time. But due to lots of inconvenience brought to us by some error existing in mobile phone positioning system, how to accurately position becomes a hot research topic at home and abroad. This paper puts forward a way of precise calculating position. Firstly, take object's image with mobile phone camera; then, process the image to extract and identify object inside; finally, calculate the distance between the object and user with the object's information stored in the database.