• Journal of the Korean Society of Clothing and Textiles
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• v.37 no.8
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• pp.1029-1043
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• 2013

The pearl is a highly valuable gem that has historically represented wealth and power. Pearl necklaces have developed intro various types and represent an essential status item for modern women. This study first examines the symbolic and various meanings of pearls. Second, this study examines wearing types and pearl necklace patterns based on historical figures and modern fashion icons famous for personal displays of pearls. This study examines and analyzes various specialty publications about jewels, history of costumes, fashion magazines, academic research data, and internet search results. The conclusion of this study is as follows. Pearls have various symbolic meanings that are unlike other gems. Pearls represent purity, innocence, marital fidelity, an intimate relationship with the moon, frozen tears of God, solitude, triumph over adversity, wisdom, and sensual attraction. The societies and people traditionally famous for pearls were the Roman Empire, Queen Cleopatra of Egypt, Queen Theodora of the Byzantine Empire, Queen Elizabeth I, Queen Marie Antoinette, Empress of Eugenie Napoleon III, and Queen Alexandra. They showed a special affection for pearl necklaces and various wearing patterns unique to the time. Their pearl necklaces became a historic and symbolic legacy. Reestablished through the costume jewelry of cultivated pearls designed by Coco Chanel in the $20^{th}$ century, the pearl necklace has showed a variety of fashion trends in addition to a traditional symbolism of wealth and power. Josephine Baker, Louise Brooks, Marilyn Monroe, Elizabeth Taylor, Jacqueline Kennedy, Queen Elizabeth II, Princess Diana, Michelle Obama and Sarah Jessica Parker have worn notable pearl necklaces and established an individual style that utilizes the adornment of fashionable and stylish pearl necklaces. They have worn pearl necklaces while applying various fashion trend motifs to symbolic pearl features of that have changed the perception of the pearl and themselves.

• Journal of the Korea Institute of Building Construction
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• v.21 no.5
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• pp.397-408
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• 2021

The construction industry has the highest occupational accidents/injuries and has experienced the most fatalities among entire industries. Korean government installed surveillance camera systems at construction sites to reduce occupational accident rates. Construction safety managers are monitoring potential hazards at the sites through surveillance system; however, the human capability of monitoring surveillance system with their own eyes has critical issues. A long-time monitoring surveillance system causes high physical fatigue and has limitations in grasping all accidents in real-time. Therefore, this study aims to build a deep learning-based safety monitoring system that can obtain information on the recognition, location, identification of workers and heavy equipment in the construction sites by applying multiple object tracking with instance segmentation. To evaluate the system's performance, we utilized the Microsoft common objects in context and the multiple object tracking challenge metrics. These results prove that it is optimal for efficiently automating monitoring surveillance system task at construction sites.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1111-1120
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• 2022

Image Capturing is a matter of viewing images and describing images in language. The problem is an important problem that can be solved by keeping, understanding, and bringing together two areas of image processing and natural language processing. In addition, by automatically recognizing and describing images in text, images can be converted into text and then into speech for visually impaired people to help them understand their surroundings, and important issues such as image search, art therapy, sports commentary, and real-time traffic information commentary. So far, the image captioning research approach focuses solely on recognizing and texturing images. However, various environments in reality must be considered for practical use, as well as being able to provide image descriptions for the intended purpose. In this work, we limit the universally available Korean and English image captioning models and text generation techniques for the purpose of image captioning.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.1976-1995
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• 2023

The anchor-free object detector CenterNet regards the object as a center point and predicts it based on the Gaussian circle region. For each object's center point, CenterNet directly regresses the width and height of the objects and finally gets the boundary range of the objects. However, the critical range of the object's center point can not be accurately limited by using the Gaussian circle region to constrain the prediction region, resulting in many low-quality centers' predicted values. In addition, because of the large difference between the width and height of different objects, directly regressing the width and height will make the model difficult to converge and lose the intrinsic relationship between them, thereby reducing the stability and consistency of accuracy. For these problems, we proposed a center point prediction method based on the Gaussian elliptic region and a size component regression method based on the small solution space. First, we constructed a Gaussian ellipse region that can accurately predict the object's center point. Second, we recode the width and height of the objects, which significantly reduces the regression solution space and improves the convergence speed of the model. Finally, we jointly decode the predicted components, enhancing the internal relationship between the size components and improving the accuracy consistency. Experiments show that when using CenterNet as the improved baseline and Hourglass-104 as the backbone, on the MS COCO dataset, our improved model achieved 44.7%, which is 2.6% higher than the baseline.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.701-720
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• 2023

Aerial objects are more challenging to segment than normal objects, which are usually smaller and have less textural detail. In the process of segmentation, target objects are easily omitted and misdetected, which is problematic. To alleviate these issues, we propose local aggregation feature pyramid networks (LAFPNs) and pyramid integrated context modules (PICMs) for aerial object segmentation. First, using an LAFPN, while strengthening the deep features, the extent to which low-level features interfere with high-level features is reduced, and numerous dense and small aerial targets are prevented from being mistakenly detected as a whole. Second, the PICM uses global information to guide local features, which enhances the network's comprehensive understanding of an entire image and reduces the missed detection of small aerial objects due to insufficient texture information. We evaluate our network with the MS COCO dataset using three categories: airplanes, birds, and kites. Compared with Mask R-CNN, our network achieves performance improvements of 1.7%, 4.9%, and 7.7% in terms of the AP metrics for the three categories. Without pretraining or any postprocessing, the segmentation performance of our network for aerial objects is superior to that of several recent methods based on classic algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1841-1857
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• 2023

CenterNet, a novel object detection algorithm without anchor based on key points, regards the object as a single center point for prediction and directly regresses the object's height and width. However, because the objects have different sizes, directly regressing their height and width will make the model difficult to converge and lose the intrinsic relationship between object's width and height, thereby reducing the stability of the model and the consistency of prediction accuracy. For this problem, we proposed an algorithm based on the regression of the diagonal half-length and the center angle, which significantly compresses the solution space of the regression components and enhances the intrinsic relationship between the decoded components. First, encode the object's width and height into the diagonal half-length and the center angle, where the center angle is the angle between the diagonal and the vertical centreline. Secondly, the predicted diagonal half-length and center angle are decoded into two length components. Finally, the position of the object bounding box can be accurately obtained by combining the corresponding center point coordinates. Experiments show that, when using CenterNet as the improved baseline and resnet50 as the Backbone, the improved model achieved 81.6% and 79.7% mAP on the VOC 2007 and 2012 test sets, respectively. When using Hourglass-104 as the Backbone, the improved model achieved 43.3% mAP on the COCO 2017 test sets. Compared with CenterNet, the improved model has a faster convergence rate and significantly improved the stability and prediction accuracy.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.1-10
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• 2024

Individuals with visual impairments face numerous challenges in their daily lives, with navigating streets and public spaces being particularly daunting. The inability to identify safe crossing locations and assess the feasibility of crossing significantly restricts their mobility and independence. Globally, an estimated 285 million people suffer from visual impairment, with 39 million categorized as blind and 246 million as visually impaired, according to the World Health Organization. In Saudi Arabia alone, there are approximately 159 thousand blind individuals, as per unofficial statistics. The profound impact of visual impairments on daily activities underscores the urgent need for solutions to improve mobility and enhance safety. This study aims to address this pressing issue by leveraging computer vision and deep learning techniques to enhance object detection capabilities. Two models were trained to detect objects: one focused on street crossing obstacles, and the other aimed to search for objects. The first model was trained on a dataset comprising 5283 images of road obstacles and traffic signals, annotated to create a labeled dataset. Subsequently, it was trained using the YOLOv8 and YOLOv5 models, with YOLOv5 achieving a satisfactory accuracy of 84%. The second model was trained on the COCO dataset using YOLOv5, yielding an impressive accuracy of 94%. By improving object detection capabilities through advanced technology, this research seeks to empower individuals with visual impairments, enhancing their mobility, independence, and overall quality of life.

• Horticultural Science & Technology
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• v.19 no.4
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• pp.483-487
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• 2001

This study was carried out to investigate the role of attached leaflet on rooting and growth by one nodal cutting in tomato. As cutting sources 4 to 6 nodes could be obtained from one nursery seedling. Medium for cutting was a mixture with perlite plus peat moss (1 : 1, v/v), and each plug capacity was 30mL in the 72 cells-plug tray. Plant height and the number of leaves were significantly increased by attached leaflet cutting in 'Pepe' and 'Coco' cherry tomato. Dry weights of top and root were increased as much as 3 to 15 times in the cutting attached leaflet. Rooting percentage was 93.5% in one node cutting and 86% in the cutting with hypocotyl node part. At 20 to 24 days after nodal cutting, healthy nursery plant could be produced to transplant in field. The nursery plants by nodal cutting (NPNC) showed more fibrous roots and less tap roots than that of seedling. In terms of rooting pattern, NPNC rooted at the whole ground stem part, while rooting of seedling occurred at basal part of hypocotyl.

• Korean journal of applied entomology
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• v.58 no.4
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• pp.381-387
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• 2019

From 2017 to 2019, the ovipositional and the developmental characteristics of Gampsocleis ussuriensis Adelung in the Buan area of Jeonbuk Province were examined. G. ussuriensis were mostly found in the weeded areas around the reservoir, where the adults first appeared in mid-July, showed up where by the early September, and overwintered in eggs. Nymphs appeared in early April to mid-July the next year. The nymphs hatched from early April and adults appeared after molting five times. The ovipositional period of G. ussuriensis was approximately 58 days. The total number of eggs per female was 124. The mean longevity of adults was 95.6 days for females and 84.8 days for males. Ovipositional mats were best with mixed Masato and Coco-Pitt at a ratio of 7:3. Developmental period of G. ussuriensis nymphs was 64.1 days at 24℃ and was longer than at different temperatures. The higher the temperature, the shorter the developmental period. The survival rate of nymphs was the best at 32℃ in 77.8%. The higher the density while rearing, the lower the survival rate, and the faster the development and molting velocity.

• Ocean and Polar Research
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• v.31 no.1
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• pp.31-50
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• 2009

The demarcation of Maritime Boundary is directly related to the expansion of jurisdiction and the securing of resources. Resource diplomacies of the three countries Korea, China and Japan represent a major task for the national administrations : to secure resources as well as to stablize and sustain resources for future national economies. At the sea area around Korea as well, countries are fiercely competing to secure resources and to expand jurisdiction. This is evidenced by the fact that various principles and logics which are beneficial to each own country are presented through international precedents, agreement between countries and the theories of the international law scholars. They say that the conclusion of demarcation of maritime boundary for the Yellow Sea would be easy from the point that there is no dispute related to island dominion in the waters of the Korean Peninsula especially the Yellow Sea, but still we need to have a strategic approach to this issue from the point that the factors used for claiming maritime boundaries may expand the waters of a country over much. For example, the continental shelf boundary in consideration of the distribution of sedimentary deposits in the Yellow Sea which is being raised by China began from the hypothesis that the inflow of sedimentary deposits to the Yellow Sea through the rivers of China represents absolute majority, but the results of the latest studies raised questions on the hypothesis. Especially, the studies done by Martin and Yang revealed that the inflow of sedimentary deposits to the Yellow Sea from the Yellow River is approximately less than 1% of total sedimentary deposits in the Yellow Sea, and also the result of analysis on the causes and counter policy measures on the environment of Bohai, China supports the reliability of the results of such studies. From a legal aspect, the sedimentary deposits of rivers which are claimed by China represent extremely weak ground for the claim for the title of the continental shelf. The siltline claimed by China seems to be based on the Article 76-4-(a)(i) of UNCLOS. This is, however, not the definition on the title of the continental shelf but it is only a technical formula to utilize in a case where a country desires to expand the continental shelf to over 200 nautical miles. Scientific and Technical Guidelines of the Commission on the Limits of the Continental Shelf also confirm this point through the Article 2.1.2 of the Guideline. The only case in which sedimentary deposits of rivers were referred to as concrete demarcation of maritime boundary was in the which was concluded in 1986 between India and Myanmar at the Andaman Sea. In the said case, India acknowledged the boundary up to the isobath of 200m which Myanmar claimed based on the sedimentary deposits of the Irrawaddy River. It has limits as a case for acknowledging the sedimentary deposits, however, because in fact India's acknowledgment was made in exchange for the condition that Myanmar gave up the dominion of two islands which they had been claiming from India up until that time.