• Journal of Fisheries and Marine Sciences Education
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• v.27 no.2
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• pp.576-588
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• 2015

This study analyzed the influences of elementary students' usage of smartphone, computer and TV on academic attitude. Of the subjects residing in the U city to target of 10 elementary schools from the fourth grade to sixth grade, 865 students were sampled. This research made a frequency analysis and reliability analysis of the obtained date using SPSS 21.0 program were used. Research results are as follows. First, in the smartphone, computer and TV usage status of elementary school, smartphone, computer and TV were used the high frequency with which almost every commonly used, was found to be necessary to take advantage of the time to less than one hour a day, mostly alone, it has been found that a lot of online games, videos and SNS. Second, the use of smartphone, computer and TV were showed a significant effect on all sub-variables of open, self-concept, initiative, responsibility, learning enthusiasm, future orientation, creativity, self-assessment.

• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10a
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• pp.124-140
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• 1995

The merit of fuzzy rule based systems stems from their capability of encoding qualitative knowledge of experts into quantitative rules. Recent advancement in automatic tuning or self-organization of fuzzy rules from experimental data further enhances their power, allowing the integration of the top-down encoding of knowledge with the bottom-up learning of rules. In this paper, methods of self-organizing fuzzy rules and of performing defuzzification and inference is presented based on a multi-resolution radial basis function network. The network learns an arbitrary input-output mapping from sample distribution as the union of hyper-ellipsoidal clusters of various locations, sizes and shapes. The hyper-ellipsoidal clusters, representing fuzzy rules, are self-organized based of global competition in such a way as to ensute uniform mapping errors. The cooperative interpolation among the multiple clusters associated with a mapping allows the network to perform a bidirectional many-to-many mapping, representing a particular from of defuzzification. Finally, an inference engine is constructed for the network to search for an optimal chain of rules or situation transitions under the constraint of transition feasibilities imposed by the learned mapping. Applications of the proposed network to skill acquisition are shown.

• Journal of the Korean Society of Clothing and Textiles
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• v.36 no.10
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• pp.1074-1086
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• 2012

This study examines the influence of mentoring function on major adaptation of university students in a fashion related department and identifies the moderating role of self-efficacy and mentor competence on the effectiveness of a fashion mentoring function. Questionnaire data were gathered from 266 university students in a fashion related department with previous experience in a mentoring program. The results showed that the psychosocial function, sensitivity developmental function, and the fashion career developmental function had a positive impact on the department adaptation (adaptation for professor and adaptation for learning). In addition, there were moderating effects of self-efficacy on the influence of the fashion career developmental function on professor adaptation and the moderating effects of mentor competence on the influence of the sensitivity developmental function on professor adaptation. The results of this study provide various guidelines for professors or administrators of fashion related departments who are interested in mentoring systems.

• 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.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.7
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• pp.140-145
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• 1989

In this paper, we present an effective circuit placement method called SOAP (self-organization assisted placement) for rectilinear or arbitrarily-shaped region arised form the layout of ASIC (application specific integrated circuit). Self-organization is a learning algorithm for neural networks proposed by [1] which adjusts weights of synapses connected to neurons such that topologically close neurons are sensitive to inputs that are physically similar. In SOAP, we obtain a good circuit placement result in arbitrarily-shaped region by replacing the block of circuit and the position (x, y coordinates) of the block with the neuron and the weight pair of synapses connected to the neuron, respectively. This method can also be extended to the circuit placement over the nonplanar surface.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.2
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• pp.50-59
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• 1998

This paper presents stabilization and adaptive control of flexible single link robot manipulator system by self-recurrent neural networks that is one of the neural networks and is effective in nonlinear control. The architecture of neural networks is a modified model of self-recurrent structure which has a hidden layer. The self-recurrent neural networks can be used to approximate any continuous function to any desired degree of accuracy and the weights are updated by feedback-error learning algorithm. When a flexible manipulator is rotated by a motor through the fixed end, transverse vibration may occur. The motor toroque should be controlled in such a way that the motor rotates by a specified angle, while simultaneously stabilizing vibration of the flexible manipuators so that it is arresed as soon as possible at the end of rotation. Accurate vibration control of lightweight manipulator during the large changes in configuration common to robotic tasks requires dynamic models that describe both the rigid body motions, as well as the flexural vibrations. Therefore, a dynamic models for a flexible single link robot manipulator is derived, and then a comparative analysis was made with linear controller through an simulation and experiment. The results are proesented to illustrate thd advantages and imporved performance of the proposed adaptive control ove the conventional linear controller.

• The KIPS Transactions:PartB
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• v.8B no.6
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• pp.651-656
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• 2001

In this paper, we propose Improved Self-Organized Graph(Improved SOG：$SOG^*$)thinning method, which maintains the excellent thinning results of Self-organized graph(SOG) built from Self-Organizing features map and improves the performance of modified SOG using a new incremental learning method of Kohonen features map. In the experiments, this method shows the thinning results equal to those of SOG and the time complexity O((logM)3) superior to it. Therefore, the proposed method is useful for the feature extraction from digits and characters in the preprocessing step.

• The Journal of Korean Academic Society of Nursing Education
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• v.12 no.2
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• pp.229-237
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• 2006

Purpose: The purpose of this study was to investigate the influencing factors of interpersonal relationship in nursing college students for effective learning ability and teaching strategy. Method: In order to get the data by self-questionnaire, 166 subjects were selected. The instruments for this study were Preceptual Orientation Scale, Self-Efficacy Scale, Narcissistic Personality Disorder Scale, and Interpersonal Relationship Scale. The dada was analyzed by percentage, mean, standard deviation, t-test, one-way ANOVA, Scheffe' test, Pearson's correlation coefficient and Stepwise multiple regression using SPSS 12.0 program. Result: The main factors that affect interpersonal relationship were self-perception and social-efficacy. These variables were account for 37.9% of interpersonal relationship. The significant influencing factors on interpersonal relationship were self-perception, social-efficacy. Conclusion: It is necessary to develop a strategy to get positive perceptual orientation and successful interpersonal relationship for nursing college students by further studies with small group program for the best result.

• Perspectives in Nursing Science
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• v.8 no.2
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• pp.86-96
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• 2011

Purpose: This study examined the effects of simulation-based education on knowledge about and self-confidence in maternity nursing care in senior students. Methods: One group, pre-post design, was utilized with 28 students. The simulation-based maternity nursing education that consisted of two sessions each 2 hours long for intrapartum and postpartum care was provided to 4 small groups. An expert panel of 3 maternity clinical instructors developed the module with a high-fidelity maternal simulator. Core items of knowledge about and self-confidence in maternity nursing care were measured with 13 items before and after the sessions. Results: The knowledge score did not increase significantly (z=-1.95, p=.05); however, self-confidence in maternity nursing care showed a significant change in the posttest (z=-2.82, p<.001). The subjective evaluation of the students indicated that the simulation-based education was helpful in preparing for clinical practicum as far as interaction with clients, psychological readiness to practice, and learning efficiencies. Conclusion: The simulation-based nursing education was useful in improving self-confidence in clinical performance for childbirth and postpartum care in nursing students. Along with the application of diverse scenarios in simulations, modules with standard patients and role-plays are also recommended for maternity nursing practicum to empower the competency of the students.