• Women's Health Nursing
• /
• v.11 no.1
• /
• pp.67-76
• /
• 2005

Purpose: This study was to identify the influence of sexual behavior, body image, social support, and other characteristics on sexual satisfaction in patients with breast cancer according to their participation in a support group. Method: Data was collected by self-report questionnaires. Participants included 63 patients attending a support group and 76 patients who did not participate in the support group. The questionnaire sections consisted of sexual satisfaction, sexual behavior, body image, social support and information on general characteristics, disease-related characteristics, and sexual life-related characteristics. Result: There was no statistically significant difference in sexual behavior, body image and sexual satisfaction between the two groups. Social support scores were significantly higher in the support group. Sexual satisfaction was positively related with sexual behavior, post-op change of sexual intercourse frequency, body image, and patient's education level, and negatively related to age in the support group. Sexual satisfaction was positively related with sexual behavior, social support and body image in the non support group. Sexual behavior is predictable 37.0% of sexual satisfaction in the support group. Sexual behavior, body image, and social support is predictable for 38.0% of the sexual satisfaction in non support group participants. Conclusion: Implications point to the need for the development and implementation of programs that focus specifically on sexual life issues for breast cancer patients, as well as further research measuring the effects of such intervention programs. Continuous education and counseling through participation in support groups can contribute to promote and affirm a healthy sexual life for patients with breast cancer.

• Proceedings of the Korean Information Science Society Conference
• /
• 2006.10c
• /
• pp.218-221
• /
• 2006

본 논문은 인터넷의 대표적인 문제점중의 하나인 Adult Image 분류 연구에 대해 기술한다. 특히 우리는 이러한 Adult Image를 분류하기 위한 Data Set을 5가지 타입으로 구성한다. 이러한 각 Image에 대해 Color, Gradient, Edge Direction 특성의 Feature들을 추출하고 이를 Histogram으로 구성한다. 이렇게 구성된 Histogram을 Support Vector Machine에 적용하여 Adult Image를 분류한다. 그 결과, 우리는 8250개의 Test Set에 대하여 Recall(96.53%), Precision(97.33%), False Positive(2.96%), F-Measure(96.93%)의 성능 결과를 보여준다.

• Journal of Korean Academy of Nursing
• /
• v.35 no.7
• /
• pp.1343-1352
• /
• 2005

Purpose: This study was designed to identify the degree of self-image and social support among Korean-Chinese adolescents and investigate the relationship between these variables. Method: A total of 621 Korean-Chinese adolescents in five middle schools in YanBian, China were recruited from March 1st to the 9th, 2005. Data was analysed using descriptive statistics, Pearson correlation coefficient, t-test, and ANOVA with the SPSS 11.5 program. Result: In Korean-Chinese adolescents, the total self-image score was statistically different for age, parents' education status, parents' job and living with parents. In the 12 subscales, scoresof emotional tone, impulse control, sexuality, social functioning, vocational attitudes and self-reliance had significant differences between groups regarding gender. The total self-image was in the average range. However, areas of mental health and family function were lower than average and the scale of idealism washigher than average. The adolescents perceived parent's support was higher then friend's support. There was a positive correlation between self-image and social support. Conclusion: The findings suggest there is a need to examine self-image and social support of Korean- Chinese adolescents according to their parents' marital status and a need to develop a program to help these broken family's adolescents.

• Current Industrial and Technological Trends in Aerospace
• /
• v.8 no.1
• /
• pp.25-32
• /
• 2010

Utilization of KOMPSAT-2 satellite image is growing, because the resolution of KOMPSAT -2 is improved 43.5 times than that of KOMPSAT-1. To support for satellite image commercialization, KOCUST(KOMPSAT Customer & User Support Team) was composed, operation process was established and defined and support system for satellite image Commercialization was constructed. Also the support system constantly is improved for various user. In this paper, organization and function of support system developed so far these days for commercial user and operations related with it were described. In addition, direction of development was discussed

• The Journal of Korean Institute of Information Technology
• /
• v.15 no.12
• /
• pp.165-171
• /
• 2017

This paper proposes a method of support-generation using morphological image processing instead of geometric calculations. The geometric computational cost is dependent on the shape, but our method is independent on the shape. For obtaining the external support area for extrusion shape, we represents morphological operations between two sliced layer images and shows results of each operation stages. Internal support area is evaluated from erosion and opening operations with the sliced-layer image. In these support areas, the supporter image is generated using the designed support structures. Also, we made a DLP printer and the STL model included supporter-structure is printed by the DLP printer. We confirmed the necessity of support-generation method with the support structures individually dependent on materials by looking at the printed results.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.2 no.3
• /
• pp.150-170
• /
• 2008

In recent years, main focus of research on image retrieval techniques is on content-based image retrieval. Text-based image retrieval schemes, on the other hand, provide semantic support and efficient retrieval of matching images. In this paper, based on Formal Concept Analysis (FCA), we propose a new image indexing and retrieval technique. The proposed scheme uses keywords and textual annotations and provides semantic support with fast retrieval of images. Retrieval efficiency in this scheme is independent of the number of images in the database and depends only on the number of attributes. This scheme provides dynamic support for addition of new images in the database and can be adopted to find images with any number of matching attributes.

• Proceedings of the KSRS Conference
• /
• v.2
• /
• pp.808-811
• /
• 2006

The KOMPSAT-2 was developed by KARI and it was successfully launched from Plesetsk, Russia on 28th July 2006. The Korean government decided the commercialization of the KOMPSAT-2 image data and direct reception services worldwide. SPOT Image, based in Toulouse (France) was selected by KARI through an international open bidding as a foreign company for the KOMPSAT-2 image promotion over the entire world except the territory of Republic of Korea including the North Korea, the United States of America, UAE, Saudi Arabia, Kuwait, Qatar, Oman, Yemen, Egypt, Iran, Iraq, Jordan, Lebanon, and Syria. KAI (Korea Aerospace Industry Ltd.) is an engaged Korean company for this area. KARI has responsibility to operate the satellite, data acquisition, archiving for the worldwide commercialization. For the processing and delivery of the KOMPSAT-2 image data to the users of KAI and SPOT Image, KAI has the binding contract with KARI. So KAI has the responsibility for the commercial ground station operation such as user support, data processing, and the data delivery. The KOMPSAT-2 ground station is hosted in KARI, so KARI has developed the concept of KOCUST (KOMPSAT-2 Commercial User Support Team) jointly with KAI to support the data processing and delivery as KOMPSAT-2 developer and satellite operator. The main purpose of the KOCUST is to support the operational activities to provide the data and service quality to satisfy customers. KOCUST will be organized by the members of KARI and KAI together. KARI members will mainly take the role of KOCUST coordination, data processing and user support in a public sector. KAI members are going to take user desk, data validation and delivery et cetera, which are related with users. This paper describes a summarized concepts of KOCUST like organization, dedicated tasks of each part and work flow of daily operation.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.9 no.6
• /
• pp.1889-1893
• /
• 2008

This paper explores the classification performance of applying to support vector machines (SVMs) for the image classification problems. In this study, we extract the color, texture and shape features of natural images and compare the performance of image classification using each individual feature and integrated features. The experiment results show that classification accuracy on the basis of color feature is better than that based on texture and shape features and the results of the integrating features also provides a better and more robust performance than individual feature. In additions, we show that the proposed classifier of SVM based approach outperforms BPNN to corporate the image classification problems.

• Journal of Information Processing Systems
• /
• v.18 no.1
• /
• pp.48-58
• /
• 2022

Blind restoration for motion-blurred images is always the research hotspot, and the key for the blind restoration is the accurate blur kernel (BK) estimation. Therefore, to achieve high-quality blind image restoration, this thesis presents a novel windowed-total-variation method. The proposed method is based on the spatial scale of edges but not amplitude, and the proposed method thus can extract useful image edges for accurate BK estimation, and then recover high-quality clear images. A large number of experiments prove the superiority.

• Journal of the Semiconductor & Display Technology
• /
• v.10 no.3
• /
• pp.75-81
• /
• 2011

This paper presents a novel method for image classification based on a hybrid genetic algorithm (GA) and support vector machine (SVM) approach which can significantly improve the classification performance for content-based image retrieval (CBIR). Though SVM has been widely applied to CBIR, it has some problems such as the kernel parameters setting and feature subset selection of SVM which impact the classification accuracy in the learning process. This study aims at simultaneously optimizing the parameters of SVM and feature subset without degrading the classification accuracy of SVM using GA for CBIR. Using the hybrid GA and SVM model, we can classify more images in the database effectively. Experiments were carried out on a large-size database of images and experiment results show that the classification accuracy of conventional SVM may be improved significantly by using the proposed model. We also found that the proposed model outperformed all the other models such as neural network and typical SVM models.