• Journal of the Korea Fashion and Costume Design Association
• /
• v.25 no.3
• /
• pp.1-16
• /
• 2023

The purposes of this study were to investigate bag purchasing behaviors and design preferences of male and female adult consumers, and to find the differences depending on sex and age variable. A survey was conducted on 400 male and female adults from 20s to 50s. The questionnaire consisted of bag purchase behaviors, bag design preferences, and the subjects' demographic characteristics. The data were analyzed by Cronbach's α, factor analysis, x² test and t-test using SPSS. The results were as follows. First, as bag selection criteria, four factors (practicality, symbolism, aesthetics, and economics) were derived, and adult consumers considered economics as the most important among the factors. As for purchasing information sources, three factors (media, human resources, and store) were derived, and adult consumers considered human resources and store information sources more important than media. The main motive for purchasing bags was age and damage of the owned products, and Internet shopping malls were the most common purchasing place. The average annual cost of purchasing bags was 100,000 to 300,000 won, and the frequency of purchase was about once a year. Second, as bag preference images, four factors (individual, romantic, active, and classic image) were derived, and adult consumers preferred classic images the most. The shoulder bag was the most preferred as the bag shape, and black was the most preferred bag color. For the material, natural leather was the most preferred, and for the size, medium size was the most preferred. Third, bag purchasing behaviors and design preferences showed many significant differences according to the sex and age of the consumers. Therefore, the results of this study suggests that bag companies need to establish product development and marketing strategies in consideration of differences according to the sex and age group of adult consumers.

• Fashion & Textile Research Journal
• /
• v.25 no.6
• /
• pp.732-738
• /
• 2023

This study delves into the realm of upcycling beauty design by repurposing discarded CDs, magazines, and fabrics. The study outlines a meticulous process for transforming waste into beauty designs. We created three upcycling beauty design works as part of this investigation. The first creation, called Silver Leaf, uses the silver section of CDs to craft leaves and stems. Achromatic colors are used as makeup to achieve cyber-inspired imagery. After carrying out silver-gray eye makeup, the lips were completed by affixing a CD component. The second creation is a firebird crafted by cutting or folding fashion magazines to create essential items. The colorful firebird image was completed using vivid color makeup of shades such as red and yellow. After proceeding with red eye makeup, the lips were completed by cutting and pasting magazine cutouts. The third piece is a spring flower, which involved selectively cutting lace patterns to complete a beauty design extending from head to face. The colors are spring-themed and encompass pink, yellow, and blue. Pink, blue, and green eyeshadows were applied on the lace, attached from head to face, chest, and lips. This study advocates for the prospect of upcycling beauty design using sustainable materials by repurposing waste resources. It also introduces the possibilities of creative activities in this field through upcycling. The study aims to play a role in changing the perception of environmental conservation, a concern of our times, through the use of sustainable resources.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.37 no.3
• /
• pp.261-273
• /
• 2024

This paper is an experimental study on the improvement of smoke and flame detection from different materials with YOLO. For the study, images of fires occurring in various materials were collected through an open dataset, and experiments were conducted by changing the main factors affecting the performance of the fire object detection model, such as the bounding box, polygon, and data augmentation of the collected image open dataset during data preprocessing. To evaluate the model performance, we calculated the values of precision, recall, F1Score, mAP, and FPS for each condition, and compared the performance of each model based on these values. We also analyzed the changes in model performance due to the data preprocessing method to derive the conditions that have the greatest impact on improving the performance of the fire object detection model. The experimental results showed that for the fire object detection model using the YOLOv5s6.0 model, data augmentation that can change the color of the flame, such as saturation, brightness, and exposure, is most effective in improving the performance of the fire object detection model. The real-time fire object detection model developed in this study can be applied to equipment such as existing CCTV, and it is believed that it can contribute to minimizing fire damage by enabling early detection of fires occurring in various materials.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.19 no.2
• /
• pp.125-139
• /
• 2024

The corporate logo symbolizes the company's value, goals and vision as a visual symbol representing the company. It serves as a communication tool for companies by conveying different messages depending on design and color. As demands for ESG management have recently increased, companies have begun to implicitly demonstrate values such as environmental protection and corporate transparency through logos. Companies use logos as a strategy to visually emphasize the value they pursue and project the desired image as a signal. In this process, stakeholders who see the logo experience cognitive bias. Therefore, this study seeks to find out that ESG value can be indirectly communicated by the design of corporate logos, which can also affect a company's ESG evaluation. In addition, this study will examine the moderate effect that logos expect to encounter a greater bias effect as the companies actively include ESG-related issues in corporate disclosure data. This study conducted an analysis of 617 KOSPI-listed companies using ESG evaluation data from 2020 to 2022. The analysis confirmed the significant relation of the type of logo and ESG disclosure on ESG evaluation but found partially moderate effect of ESG disclosure.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.66 no.4
• /
• pp.27-39
• /
• 2024

Soil texture is determined by the proportions of sand, silt, and clay within the soil, which influence characteristics such as porosity, water retention capacity, electrical conductivity (EC), and pH. Traditional classification of soil texture requires significant sample preparation including oven drying to remove organic matter and moisture, a process that is both time-consuming and costly. This study aims to explore an alternative method by developing an AI model capable of predicting soil texture from images of pre-sorted soil samples using computer vision and deep learning technologies. Soil samples collected from agricultural fields were pre-processed using sieve analysis and the images of each sample were acquired in a controlled studio environment using a smartphone camera. Color distribution ratios based on RGB values of the images were analyzed using the OpenCV library in Python. A convolutional neural network (CNN) model, built on PyTorch, was enhanced using Digital Image Processing (DIP) techniques and then trained across nine distinct conditions to evaluate its robustness and accuracy. The model has achieved an accuracy of over 80% in classifying the images of pre-sorted soil samples, as validated by the components of the confusion matrix and measurements of the F1 score, demonstrating its potential to replace traditional experimental methods for soil texture classification. By utilizing an easily accessible tool, significant time and cost savings can be expected compared to traditional methods.

• Clinical Endoscopy
• /
• v.56 no.3
• /
• pp.315-324
• /
• 2023

Background/Aims: Image-enhanced endoscopy can detect superficial oro-hypopharyngeal squamous cell carcinoma; however, reliable endoscopy of the pharyngeal region is challenging. Endoscopy under general anesthesia during transoral surgery occasionally reveals multiple synchronous lesions that remained undetected on preoperative endoscopy. Therefore, we aimed to determine the lesion detection capability of endoscopy under general anesthesia for superficial oro-hypopharyngeal squamous cell carcinoma. Methods: This retrospective study included 63 patients who underwent transoral surgery for superficial oropharyngeal squamous cell carcinoma between April 2005 and December 2020. The primary endpoint was to compare the lesion detection capabilities of preoperative endoscopy and endoscopy under general anesthesia. Other endpoints included the comparison of clinicopathological findings between lesions detected using preoperative endoscopy and those newly detected using endoscopy under general anesthesia. Results: Fifty-eight patients (85 lesions) were analyzed. The mean number of lesions per patient detected was 1.17 for preoperative endoscopy and 1.47 for endoscopy under general anesthesia. Endoscopy under general anesthesia helped detect more lesions than preoperative endoscopy did (p<0.001). The lesions that were newly detected on endoscopy under general anesthesia were small and characterized by few changes in color and surface ruggedness. Conclusions: Endoscopy under general anesthesia for superficial squamous cell carcinoma is helpful for detecting multiple synchronous lesions.

• Journal of IKEEE
• /
• v.28 no.1
• /
• pp.65-71
• /
• 2024

This paper proposes a neural network that can reduce the number of parameters while reducing matching errors in occluded regions to increase the accuracy of depth maps in stereo matching. Stereo matching-based object recognition is utilized in many fields to more accurately recognize situations using images. When there are many objects in a complex image, an occluded area is generated due to overlap between objects and occlusion by background, thereby lowering the accuracy of the depth map. To solve this problem, existing research methods that create context information and combine it with the cost volume or RoIselect in the occluded area increase the complexity of neural networks, making it difficult to learn and expensive to implement. In this paper, we create a depthwise seperable neural network that enhances regional feature extraction before cost volume generation, reducing the number of parameters and proposing a neural network that is robust to occlusion errors. Compared to PSMNet, the proposed neural network reduced the number of parameters by 30%, improving 5.3% in color error and 3.6% in test loss.

• International Journal of Highway Engineering
• /
• v.13 no.1
• /
• pp.157-168
• /
• 2011

Recently, the cases of freeze-thaw distress on concrete pavement in domestic have been reported periodically. Hereupon, the necessity to establish the standard of spacing factor came to the fore. The test method for analyzing spacing factor is provided in the standard of ASTM C 457. Since researchers tend to judge study results subjectively, the results should be revised throughly. Image analysis program for determining the characteristics of air-void system on hardened concrete identify air void through the difference of a color. The pixel intensity values used in this program have a significant effect on the analysis results. This study compared the automated void count by varying pixel intensity values with the manual void count in order to determine the optimum range of pixel intensity values. Also, this study analyzed the air-void characteristics on eight kinds of concrete mixtures. In this study, it was confirmed that the variation of void counted manually was around 10% from the results of round robin test, and that the optimum range of pixel intensity values is around 80~90. And it was also confirmed that air content (as a whole) was increased generally and spacing factor was decreased by increasing air-entrainer content. But some concrete mixtures showed a tendency that air content was constant and spacing factor was decreased by increasing air-entrainer content. This causes the air entrained by air-entrainer has more influence on spacing factor than air content. Also, the deviation of spacing factor by cutting position of concrete specimen was about 30~100${\mu}m$ because of the limit of 2-D image analysis. The additional study about variation of spacing factor by cutting position of concrete specimen will be performed later.

• Journal of Science Education
• /
• v.38 no.3
• /
• pp.641-656
• /
• 2014

This study aimed at analyzing visual materials included in school science textbooks, specifically the textbooks for first semester courses of 3rd to 6th graders. The purpose was to provide directions for future textbooks by understanding the functions of the images in both pedagogical and social perspectives as well. The study was conducted by investigating the types, roles and socio-semiotic features of the images in science textbooks. The results were as follows. Firstly, the most used types of images in 2007 curriculum textbooks were photographs and drawn pictures. Uses of other visual aids than above were extremely rare. It was also found that as the educational level rises, the use of images for decorative functions drastically declined. The majority of the images were used in providing supplementary explanations or examples. This implies that the images effectively play the role of helping science education. In addition, more use of worksheets images was found, indicating that as educational level increases, students participate more actively in research sessions or data analysis. In socio-semiotic perspective, visual images showed high accessibility to students in 'Type of visual image', 'Function of visual image', 'Distance of shot', 'Horizontal angle of shot', 'Color moduation'. It was implied that there will a close correlation between the type, role and the socio-semiotic characteristics of visual images in textbooks. For example, photograph-type visuals were mostly used as supplementary references. And when applying the socio-semiotic analysis to photograph-type visuals, they showed 'real type', 'narrative-metaphor type', and 'shadow effect' among socio-semiotic features. Such correlations implied that knowing the type of the visual image may help determining the role of the image in the textbook to some extent, and also corresponding socio-semiotic characteristics. As a result, it was possible to infer how accessible certain visual images are to students. The above results have implications for the effective use of visual images in future textbooks.

• Journal of KIISE:Computing Practices and Letters
• /
• v.8 no.4
• /
• pp.479-488
• /
• 2002

In recent years the amount of digital video used has risen dramatically to keep pace with the increasing use of the Internet and consequently an automated method is needed for indexing digital video databases. Textual information, both superimposed and embedded scene texts, appearing in a digital video can be a crucial clue for helping the video indexing. In this paper, a new method is presented to extract both superimposed and embedded scene texts in a freeze-frame of news video. The algorithm is summarized in the following three steps. For the first step, a color image is converted into a gray-level image and applies contrast stretching to enhance the contrast of the input image. Then, a modified local adaptive thresholding is applied to the contrast-stretched image. The second step is divided into three processes: eliminating text-like components by applying erosion, dilation, and (OpenClose+CloseOpen)/2 morphological operations, maintaining text components using (OpenClose+CloseOpen)/2 operation with a new Geo-correction method, and subtracting two result images for eliminating false-positive components further. In the third filtering step, the characteristics of each component such as the ratio of the number of pixels in each candidate component to the number of its boundary pixels and the ratio of the minor to the major axis of each bounding box are used. Acceptable results have been obtained using the proposed method on 300 news images with a recognition rate of 93.6%. Also, my method indicates a good performance on all the various kinds of images by adjusting the size of the structuring element.