• Journal of the Korean Society for Railway
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• v.6 no.4
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• pp.221-231
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• 2003

Most rail system related simulators currently used are designed to simulate only one discipline system. This obviously assumes the other discipline systems are running regularly not being affected by the system being simulated. In this paper a multi discipline simulator is proposed and its design concept is presented. A multi discipline simulator is the simulator in which major subsystems with different technical discipline are simulated simultaneously. The advantage of the simulator is in that it makes it possible to analyze the systems behavior while other discipline system vary. With this we can identify the possible multi-discipline problems and even find their solutions. A proto type simulator has been developed using object oriented programming. Object concept was judged best suitable to model the various multi-discipline self-controlling railway subsystems. It was applied to the target system, which is under development by the Korea Railroad Research Institute. The test results shows it is very useful in designn verification. It could also be a good tool in research and development work to improve the system.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.5
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• pp.398-405
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• 2005

The intelligent robots that will be needed in the near future are human-friendly robots that are able to coexist with humans and support humans effectively. To realize this, robots need to recognize their position and posture in known environment as well as unknown environment. Moreover, it is necessary for their localization to occur naturally. It is desirable for a robot to estimate of his position by solving uncertainty for mobile robot navigation, as one of the best important problems. In this paper, we describe a method for the localization of a mobile robot using image information of a moving object. This method combines the observed position from dead-reckoning sensors and the estimated position from the images captured by a fixed camera to localize a mobile robot. Using a priori known path of a moving object in the world coordinates and a perspective camera model, we derive the geometric constraint equations which represent the relation between image frame coordinates for a moving object and the estimated robot's position. Also, the control method is proposed to estimate position and direction between the walking human and the mobile robot, and the Kalman filter scheme is used for the estimation of the mobile robot localization. And its performance is verified by the computer simulation and the experiment.

• Journal of English Language & Literature
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• v.58 no.4
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• pp.783-807
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• 2012

This study compares and contrasts Freud's "work of mourning" which mostly appears in his memorable essay "Mourning and Melancholia" and Derrida's theory of mourning which appears in various works such as MEMOIRES for Paul de Man, The Work of Mourning, and others. Freud maintains that the mourner begins to sever emotional ties to the lost object through a labor of memory and eventually completes the work of mourning. It is a "testing of reality" that motivates the mourner to begin to relinquish emotional attachment to the lost object. Derrida, however, challenges Freudian work of mourning by saying that true mourning lies in "respecting the Otherness of the Other." Derrida suggests that Freud's "normal work of mourning" is "unjust betrayal" of the lost object because it "kills" and "devours" the other and thereby makes it part of the self. So he proposes that work of mourning has "to fail in order to succeed": "success fails" and "failure succeeds." There is an enormous, even epistemological, chasm between Freud who states that mourning, "however painful it may be, comes to a spontaneous end" and Derrida who states that "mourning is interminable. Inconsolable. Irreconcilable." and "I mourn Therefore I am." The former is the voice of "testing of reality" and common sense whereas the latter is that of utopian ethical vision. Yet neither seems to get the upper hand and they are kind of forced to maintain an ongoing dialogue with each other, for true mourning seems to lie somewhere in between.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1315-1318
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• 2003

In this paper, a neural network based approach using a self-organizing feature map is proposed for the segmentation of X ray images. A number of algorithms based on such approaches as histogram analysis, region growing, edge detection and pixel classification have been proposed for segmentation of general images. However, few approaches have been applied to X ray image segmentation because of blur of the X ray image and vagueness of its edge, which are inherent properties of X ray images. To this end, we develop a new model based on the neural network to detect objects in a given X ray image. The new model utilizes Mumford-Shah functional incorporating with a modified adaptive SOFM. Although Mumford-Shah model is an active contour model not based on the gradient of the image for finding edges in image, it has some limitation to accurately represent object images. To avoid this criticism, we utilize an adaptive self organizing feature map developed earlier by the authors.[1] It's learning rule is derived from Mumford-Shah energy function and the boundary of blurred and vague X ray image. The evolution of the neural network is shown to well segment and represent. To demonstrate the performance of the proposed method, segmentation of an industrial part is solved and the experimental results are discussed in detail.

• The KIPS Transactions:PartB
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• v.15B no.5
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• pp.407-412
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• 2008

Color segmentation is one of the most challenging problems in image processing especially in case of handling the images with cluttered background. Great amount of color segmentation methods have been developed and applied to real problems. In this paper, we suggest a new methodology. Our approach is focused on background extraction, as a complimentary operation to standard foreground object segmentation, using self-organizing feature selective property of unsupervised self-learning paradigm based on the competitive algorithm. The results of our studies show that background segmentation can be achievable in efficient manner.

• The Journal of Korean Association of Computer Education
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• v.11 no.2
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• pp.79-87
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• 2008

The self-regulated learning was making an alternative idea to improve a learning effect of student who takes part in a study course. This research aimed the self- regulated learning of E-learning system. Also. in this system, the students were able to select the learning object according to the learning method for improving learning effect that students take part in study spontaneously. And the learning logic was implemented for development efficiency and operation by web service. Finally, the test result of study group who had the similar studying level displayed that the proposal method was better learning effect than exist one.

• Korean Journal of Human Ecology
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• v.18 no.3
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• pp.641-656
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• 2009

The purpose of this study was to compare Chinese-Korean adolescents' with Korean adolescents's academic achievement motivation, parental expectation, teacher expectation, self-efficacy. The object is 492 students in the middle school 2th and high school 2th grade who are from Chinese-Korean school and Korean school in Seoul and Incheon. The major results of findings were as follows: First, Chinese-Korean adolescents were more high than academic achievement motivation in Korean adolescents. Second, juniors in middle school were more positive than parental expectation in high school. Third, Chinese-Korean adolescents were more positive than teacher expectation in Korean adolescents, and juniors in middle school are more positive than ones in high school. Forth, Chinese-Korean adolescents were more high than self-efficacy in Korean adolescents.

• Journal of Digital Convergence
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• v.13 no.2
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• pp.361-369
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• 2015

For the research object of this study 112 university shooting athletes, registered with Korea Shooting Federation in 2014, have been selected. Statistics processing was carried out through SPSS 16.0 and AMOS 7.0. The following results were obtained by analysis of frequency, exploratory factor analysis, confirmatory factor analysis, correlation analysis and structural equation model analysis to serve it's purpose. First, task orientation had passive influence on self-management. Second, task orientation and ego-orientation had passive influence on perceived exercise performance. Third, self-management had passive influence on perceived exercise performance.

• Journal of Industrial Technology
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• v.21 no.B
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• pp.91-98
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• 2001

In this paper, center detection and motion analysis of a moving object are studied. Kohonen's self-organizing neural network models are used for the moving objects tracking and time delay neural networks are used for dynamic characteristic analysis. Instead of objects brightness, neuron projections by Kohonen Networks are used. The motion of target objects can be analyzed by using the differential neuron image between the two projections. The differential neuron image which is made by two consecutive neuron projections is used for center detection and moving objects tracking. The two differential neuron images which are made by three consecutive neuron projections are used for the moving trajectory estimation. It is possible to distinguish 8 directions of a moving trajectory with two frames and 16 directions with three frames.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.4
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• pp.303-312
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• 2017

This paper indirectly created high density point cloud data using unmanned aerial vehicle image. Then, we tried to suggest new concept of classification technique where particular objects from point cloud data can be selectively classified. For this, we established the classification technique that can be used as search factor in classifying color information in point cloud data. Then, using suggested classification technique, we implemented object classification and analyzed classification accuracy by relative comparison with self-created proof resource. As a result, the possibility of point cloud data classification was observable using the image's information. Furthermore, it was possible to classify particular object's point cloud data in high classification accuracy.