• Journal of Korean Dental Hygiene Science
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• v.6 no.2
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• pp.161-172
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• 2023

Background: The purpose of this study is to assess the degree of self-awareness, recognition of others, and adaptation to college life of dental hygiene students, and to identify the factors affecting college life adjustment. Methods: The effect size was calculated using the G*power 3.1.9.4 program, based on the data of 191 people who agreed to participate in the questionnaire. Independent samples t-test, one way ANOVA, Post HOC Scheffe, correlation analysis, and multiple regression analysis were conducted to explore the difference and correlation between self-awareness, perception of others, and college life adjustment according to general characteristics. Results: A significant positive correlation was found in self-awareness (r=.301). The highest positive correlations were with social adjustment in both private (r=.340) and public self-awareness (r=.331) (p<0.01). Recognition of others (r=-.051) showed a negative correlation with college life adjustment, though it was not statistically significant. As factors for adapting to college life, private self-awareness (β=.232) and public self-awareness (β=.254) had a positive (+) effect, and internal self-awareness (β=-.205) was found to have a negative (-) effect (p<0.01) Conclusion: The self-awareness and recognition of others among dental hygiene students influence their smooth adjustment to college life. Improvement measures are recommended to enhance adaptability to college life and develop core competencies related to their major

• Journal of Korea Multimedia Society
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• v.7 no.4
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• pp.467-473
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• 2004

Caption in video plays an important role that delivers video content. Existing caption region extraction methods are difficult to extract caption region from background because they are sensitive to noise. This paper proposes the method to extract caption region in sports video using multiple frame merge and MBR(Minimum Bounding Rectangles). As preprocessing, adaptive threshold can be extracted using contrast stretching and Othu Method. Caption frame interval is extracted by multiple frame merge and caption region is efficiently extracted by median filtering, morphological dilation, region labeling, candidate character region filtering, and MBR extraction.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2005.11a
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• pp.191-194
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• 2005

본 논문에서는 방송 영상에서 조명효과와 크기변화 등에 강인한 얼굴패턴 검출기법을 제시한다. 제안된 얼굴검출 모델은 영상 전처리 과정과 얼굴패턴 검출 과정으로 이루어진다. 전처리 과정은 조명변화에 대한 보정기능과 다중필터에 의한 후보영역 선별기능으로 구분된다. 얼굴패턴 검출과정은 다단계의 특징지도 생성과정과 패턴분류 과정으로 이루어진다. 특징지도를 생성하기 위하여 가보(Gabor) 필터계층을 포함하는 CNN(Convolutional Neural Networks)모델을 도입하였다. 다양한 배경을 고려한 효과적인 학습을 위하여 본 논문에서는 억제성의 뉴런(Inhibitory neuron)을 포함하는 구조의 CNN모델을 적용한다. CNN으로부터 추출되는 특징집합은 최종 단계에서 WFMM(Weighted Fuzzy Min Max) 모델을 사용하여 분류된다. 이때 사용되는 특징집합의 크기는 분류기의 규모 및 계산량의 결정적인 역할을 준다. 이에 본 연구에서는 최종 분류 과정에 사용되는 특징의 수를 효과적으로 줄이기 위해 FMM모델을 사용하는 적응적인 특징 선별 기법을 제안한다. 또한 실제 영상을 통한 실험결과로부터 제안된 이론의 타당성을 고찰한다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.2
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• pp.120-126
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• 2009

In order to reduce the noise quantity in a conversation under the noisy environment it is necessary for the signal processing system to process adaptively according to the noise quantity in order to enhance the performance. Therefore this paper presents a recognition method for noise quantity by linear predictive coefficient using a three layered neural network, which is trained using three kinds of speech that is degraded by various background noises. The performance of the proposed method for the noise quantity was evaluated based on the recognition rates for various noises. In the experiment, the average values of the recognition results were 98.4% or more for such noise using Aurora2 database.

• The KIPS Transactions:PartB
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• v.18B no.4
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• pp.173-180
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• 2011

This paper shows an approach for real-time object segmentation on GPU (Graphics Processing Unit) using CUDA (Compute Unified Device Architecture). Recently, many applications that is monitoring system, motion analysis, object tracking or etc require real-time processing. It is not suitable for object segmentation to procedure real-time in CPU. NVIDIA provide CUDA platform for Parallel Processing for General Computation to upgrade limit of Hardware Graphic. In this paper, we use adaptive Gaussian Mixture Background Modeling in the step of object extraction and CCL(Connected Component Labeling) for classification. The speed of GPU and CPU is compared and evaluated with implementation in Core2 Quad processor with 2.4GHz.The GPU version achieved a speedup of 3x-4x over the CPU version.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.8
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• pp.1819-1826
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• 2010

This paper proposes the image segmentation algorithm to extracts a fish object from a fish image for fish image retrieval. The conventional algorithm using gray level similarity causes wrong image segmentation result in the boundary area of the object and the background with similar gray level. The proposed algorithm uses the reinforced edge and the adaptive block-based threshold for the boundary area with weak contrast and the virtual object to improve the eroded or disconnected object in the boundary area without contrast. The simulation results show that the percentage of extracting the visual-fine object from the test images is under 90% in the conventional algorithm while it is 97.7% in the proposed algorithms.

• Journal of The Korean Association For Science Education
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• v.28 no.8
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• pp.848-859
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• 2008

The purposes of this study were to investigate the relationship between science achievement and student-related variables in the 2006 National Assessment of Educational Achievement (NAEA), the subjects of which included 3% of students within the entire population of the grades 6, 9 and 10. The results showed that the more they talk with parents, study by themselves, and read the books, the higher the students achieved in science. The science achievement was also significantly and positively related to self-regulated learning, adaptation to school life and attitude toward science. It is implied that the approach of stimulating internal motive such as interest, attitude toward science and human relations is more effective in resulting in the students' higher science achievement than focusing on external attitudes such as forcing good study habits.

• Korean Journal of Psychosomatic Medicine
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• v.11 no.2
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• pp.182-195
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• 2003

Background: Marriage is one of the major life events and the primary source of individual happiness and meaning of life. It is not possible to predict who will marry whom and which marriage will be successful. Marital adjustment has significant influences on an individual and relationship functioning and is associated with mental health. Good marital quality may imply good general relationship. There are several factors that correlate with or predict good marital quality. Especially individual personality factors may be a major factor in achieving and maintaining marital stability, satisfaction and happiness. The aim of the present study was to investigate the possible relationship between dyadic adjustment and personality dimensions in urban married women. Methods: Dyadic Adjustment Scale, Eysenck's Personality Questionnaire, Beck Depression Inventory and State-Trait Anxiety Inventory were administered to 215 married women. Data analysis was based on 170 women who provided reliable informations. The descriptive statistics of demographic data and 4 scales were obtained. Then the possible relationships between each data were tested by calculating Pearson's product moment correlations. To test the effects of depression and anxiety on dyadic adjustment, stepwise multiple regression analysis was done. Results: Age and length of marriage were negatively correlated with affectional expression and dyadic cohesion. Educational level was positively correlated with total dyadic adjustment score and dyadic satisfaction. Family income was positively correlated with total dyadic adjustment score, dyadic consensus, dyadic satisfaction and dyadic cohesion. Different family type showed different total dyadic adjustment score, dyadic satisfaction and dyadic cohesion. Psychoticism was negatively correlated with total dyadic adjustment score, dyadic consensus, dyadic satisfaction, affectional expression and dyadic cohesion. Neuroticism was negatively correlated with total dyadic adjustment score, dyadic consensus, dyadic satisfaction and affectional expression. Extraversion and lie were not correlated with any factors. Beck Depression Inventory and State-Trait Anxiety Inventory scores were negatively correlated with total dyadic adjustment score, dyadic consensus, dyadic satisfaction, affectional expression and dyadic cohesion. The result of multiple regression analysis indicated that psychoticism was correlated with dyadic adjustment. Conclusion: These results showed that the demographic factors such as age, length of marriage, educational level, family income and family type were significantly correlated with dyadic adjustment. Psychoticism and neuroticism measured by Eysenck Personality Inventory were significantly correlated with dyadic adjustment. But the correlations with extraversion and lie were not significant. Especially correlation between neuroticism and dyadic adjustment seemed to be mediated by emotional state such as depression and anxiety. These findings suggest that personality factors may be involved in marital relationship and that clinician must consider personality aspect in dealing with marital problems. Future study about differences between control group and psychiatric patient group will be needed.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.494-499
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• 2012

In this paper, a new method for detecting various objects that can be risks to safety navigation in sea environment is proposed. By analysing Infrared(IR) images obtained from various sea environments, we could find out that object regions include both horizontal and vertical direction edges while background regions of sea surface mainly include vertical direction edges. Therefore, we present an approach to detecting object regions considering horizontal and vertical edges. To this end, in the first step, image enhancement is performed by suppressing noises such as sea glint and complex clutters using a statistical filter. In the second step, a horizontal edge map and a vertical edge map are generated by 1-D Discrete Cosine Transform technique. Then, a combined map integrating the horizontal and the vertical edge maps is generated. In the third step, candidate object regions are detected by a adaptive thresholding method. Finally, exact object regions are extracted by eliminating background and clutter regions based on morphological operation.

• Journal of the Korea Society of Computer and Information
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• v.13 no.4
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• pp.97-103
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• 2008

This study is to present a fainting motion recognizing method by using fish-eye lens images to sense emergency situations. The camera with fish-eye lens located at the center of the ceiling of the living room sends images, and then the foreground pixels are extracted by means of the adaptive background modeling method based on the Gaussian complex model, which is followed by tracing of outer points in the foreground pixel area and the elliptical mapping. During the elliptical tracing, the fish-eye lens images are converted to fluoroscope images. the size and location changes, and moving speed information are extracted to judge whether the movement, pause, and motion are similar to fainting motion. The results show that compared to using fish-eye lens image, extraction of the size and location changes. and moving speed by means of the conversed fluoroscope images has good recognition rates.