• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.35 no.6
• /
• pp.164-172
• /
• 2023

The grain size distribution of sand provides crucial information for understanding coastal erosion and sediment deposition. The commonly used sieve analysis for grain size distribution analysis has limitations such as time-consuming processes and the inability to obtain information about individual particle shapes and colors. In this study, we propose a grain size distribution analysis method using smartphone digital images, which is simpler and more efficient than the sieve analysis method. During the image analysis process, we effectively detect particles from relatively low-resolution smartphone digital images by extracting particle boundaries through image gradient calculation. Using samples collected from four beaches in Gyeongsangbuk-do, we compare and validate the proposed boundary extraction image analysis method with the analysis method that does not extract boundaries, against sieve analysis results. The proposed method shows an average error rate of 8.21% at D₅₀, exhibiting a 65% lower error compared to the method without boundary extraction. Therefore, grain size distribution analysis using smartphone digital images is convenient, efficient, and demonstrated accuracy comparable to sieve analysis.

• Journal of Science Education
• /
• v.48 no.1
• /
• pp.63-73
• /
• 2024

Knowing the learner's image of a subject-related occupation is good data for determining the direction of a teacher's teaching and learning. Existing drawing image analysis tools have the limitation that it takes a long time to analyze images and drawings of a scientist's appearance. The semantic differential method is a widely used method to analyze images of specific objects. However, research using the semantic differential method has the limitation of failing to reflect terms or factors that change over time by using the adjective pairs used in the initial study as they were in accordance with the research content. In this study, we use the semantic differential method to develop a tool to measure middle school students' scientist image and apply it to middle school students to discuss educational implications regarding the usefulness of measuring scientist image.

• Journal of Korea Society of Industrial Information Systems
• /
• v.11 no.2
• /
• pp.69-78
• /
• 2006

This paper is concerned with analyzing the images of Korean automobiles using, so called, sensory engineering, which adapts the sensory and subjective assessment of human beings in evaluating the quality of product. The methodology of analysis is suggested in this paper according to the following steps. First, 14 pairs of adjectives, which describe the image of object cars in view of the semantic differential method, are derived from consulting with several expert panels. Nextly, factor analysis is performed in order to obtain the axises, by which the images space of the object automobiles are specified, and then the images of the object automobiles are measured by the coordinate of all the object automobiles in the image space. In this paper, a sensory estimation experiment is performed to a panel consisting with In undergraduate students residing in the region of Daegu. From the result of analysis of this paper, target images, which the automobile manufacturers are intended, are achieved by and large except one company.

• Journal of The Korean Association For Science Education
• /
• v.38 no.5
• /
• pp.753-763
• /
• 2018

The image of scientists that learners have has an important impact on science learning and on science-related career choices. The image of the scientist was mainly analyzed using the drawing analysis method. Drawing analysis has limitations on drawing, mainly analyzing the external image of scientist. Science teachers' images of scientists and their perception of science-related careers are important factors in students' science learning and science-related career choices. However, research on science teachers is lacking. Therefore, the purpose of this study is to investigate the usefulness of measurement tools by developing and applying a scientist image measurement tool through the semantic analysis method, and to discuss the educational implications of the research by investigating the image of scientists and science-related professions of secondary pre-service science teachers. The subjects of the study were 79 male and 55 female for a total of 134 students in the 2nd and 3rd grades majoring in science education at a teachers college. The results of the research show that the image measurement tool consisted of four components: 'ability,' 'evaluation,' 'activity,' and 'emotion,' in 24 items. As a result of applying the developed measurement tool to the secondary pre-service science teachers, the image of the 'evaluation,' 'ability,' and 'activity' elements of the scientist were high, but 'emotion' was low. There was no statistically significant difference according to gender. It is found that science-related career perceive them as 'hard,' 'professional,' 'smart,' and 'complex.' In particular, male students perceive themselves as 'hard and difficult' while female students perceive it as 'challenging and complicated'. Finally, we discussed the usefulness of using the image measurement tool of the scientists, the image of the scientists of the secondary pre-service science teachers, and the educational implications on science-related career.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
• /
• 2002.05a
• /
• pp.295-299
• /
• 2002

본 연구에서는 피륙의 물리화학적 특성에 의해 결정되는 촉감, 태 이외에도 색채, 무의 등 여러 요소들의 영향을 받아 복합적으로 표현되는 의류소재의 총체적인 개념인 의류소재 이미지는 어떤 것들이 있으며 이러한 이미지들은 어떻게 분류될 수 있는지를 알아보기 위하여 의류소재 이미지의 평가를 위한 축을 개발해 보았다. 1995년부터 2000년까지의 Texjournal과 인터패션플래닝에서 발간되는 98/99FW부터 0255까지 트렌드 북에서 소재를 설명하는 형용사를 조사하여 유사한 형용사를 통합 처리하여 87개의 형용사를 최종 추출하여 형용사쌍을 만들고 소재 자극 없이 형용사쌍이 주는 소재이미지만을 가지고 쌍비교법을 통해 유사성을 7점 척도로 표시하도록 하였다. 얻어진 결과를 다차원척도법을 이용하여 분석하여 87개의 형용사의 평가차원을 살펴보았다. 의류소재 이미지를 평가하는 축을 다차원 척도법을 이용하여 개발한 결과 '남성적-여성적', '새로운-낡은 듯한', '캐주얼-클래식', '모호한-정돈된'의 4가지 차원의 8개축이 개발되었다.

• Journal of Information Management
• /
• v.40 no.2
• /
• pp.71-94
• /
• 2009

Indexing images using traditional indexing methods like taxonomy is not always efficient because of its visual content. This study examined how to apply folksonomies to image retrieval. To do this, first, we developed a category model for image tags found in Flickr. The model includes five categories and seventeen subcategories. Second, in order to evaluate the usefulness of the model to represent the various image tags as well as to investigate the end-user tagging behavior, three researchers classified the sampled image tags(141 most popular tags, 105 tags on three individual tag clouds and 3,848 image tags assigned on 156 images) according to the model. Finally, based on the research results, we proposed three methods for efficient image retrieval: extending folksonomies by combining them with ontologies; improving image retrieval efficiency using visual content and folksonomies; and updating taxonomy using folksonomies.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
• /
• 2010.04a
• /
• pp.67-67
• /
• 2010

이미지를 이용한 도공층 구조 분석은 도공층의 실제 Morphology를 분석하여 평가하는 방법으로서 최근 세밀한 도공층 구조 분석을 위해 이 방법에 대한 많은 연구가 진행되고 있다. 특히 이러한 방법은 수은압입법(Mercury intrusion)이나 질소흡착법 (Nitrogen adsorption by BJH theory) 등과 같은 기존의 공극 특성 평가 방법과 달리 pore aspect ratio 및 orientation 등과 같은 공극 dimension을 평가할 수 있는 장점이 있다. 이러한 공극 dimension은 size distribution 및 porosity와 더불어 인쇄, 라미네이션 접착 등과 같은 Liquid interfacial 및 침투 측면에서 중요한 요소이기 때문에 이를 평가하기 위한 적합한 방법으로 인식되고 있다. 또, 원지 부분과 도공층 간의 경계를 명확하게 보여주고 Surface와 Cross-section 영역을 구분하여 평가 할 수 있어 더 명확한 평가를 가능하게 한다. 본 연구에서는 이미지 분석을 통해 도공액 구성 조건에 따른 도공층의 공극 구조 특성을 평가 하였고 일부 요소에 대해서는 수은 압입법과 비교 평가하여 이미지 분석법과의 상관성에 대해 고찰 하였다. 본 연구에서 사용된 FE (Field Emission)-SEM은 일반 SEM과 달리 전압에 의한 높은 전기장의 형성을 통해 저 가속 전압으로 이미지를 구현하는 장비로서 본 연구에서는 FE-SEM을 통해 도공층의 세밀한 Morphology와 공극 구조 이미지를 구현할 수 있었다.

• Journal of the Korean earth science society
• /
• v.25 no.8
• /
• pp.799-811
• /
• 2004

In this one species of planktonic foraminifers, Neogloboquadrina pachyderma, which has been collected from the sediments cores in the northeast Pacific ODP sites, was computerized through using digitalized images. The foraminiferal communities were analyzed, and the coiling direction of the N. pachyderma was determined by using computer progamming technology. In this way by appling algorithm-based method of reading, the tasks of sorting and analyzing the foraminiferal indiniduals and communities can be performad and high speed on a very large amount of specimens collected. It is found that the study had 90% accordance with the result of stereoscopic observation. This result suggested that digital image analysis could be successfully adopted in the field of micropaleontology.

• Journal of Science Education
• /
• v.44 no.2
• /
• pp.214-224
• /
• 2020

Physics image and interest are factors that influence physics learning. Freshmen enter an engineering college under various learning conditions when they were in high school. Understanding physics image and interest according to characteristics of freshmen will help college physics education. The purpose of this study is to investigate the physics image and interest of freshmen in an engineering college according to their gender and physics course completion in high school and discuss the educational implications of college students on physics learning. The subjects of the study are 664 first grade students in engineering college. We analyzed physics image and interest of students according to gender and physics course completion in high school. Physics image is analyzed using semantic analysis. As a result of the analysis, the physics image is different according to the physics course completion. Interest in Physics depends on gender and physics course completion. Finally, we discuss the educational implications of college physics learning for engineering students.

• Journal of The Korean Association of Information Education
• /
• v.21 no.5
• /
• pp.527-535
• /
• 2017

In this study, we analyzed the image of artificial intelligence recognized by elementary students using semantic differential scale. First, we extracted 23 pairs of image adjectives related to perception of artificial intelligence. Adjectives were classified into three types related to recognition, emotion and ability and 827 elementary students were examined. Image factors were classified into four factors: convenience, technological progress, human-friendliness, and concern. As a result, they showed a clear image that artificial intelligence is clever, new, and complex but exciting. In comparison with variables, female students, coding experience and older students thought that artificial intelligence was more human-friendly and technological progressive.