• Journal of Broadcast Engineering
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• v.27 no.6
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• pp.906-913
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• 2022

In this paper, we propose a new deep network architecture using nearest neighbor kernel for the estimation of dense depth map from its sparse map and corresponding color information. First, we propose to decompose the depth map signal into the structure and details for easier prediction. We then propose two separate subnetworks for prediction of both structure and details using classification and regression approaches, respectively. Moreover, the nearest neighboring kernel method has been newly proposed for accurate prediction of structure signal. As a result, the proposed method showed better results than other methods quantitatively and qualitatively.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.73-80
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• 2021

Nowadays microblogs have become the most popular platforms to obtain and spread information. Twitter is one of the most used platforms to share everyday life event. However, rumors and misinformation on Arabic social media platforms has become pervasive which can create inestimable harm to society. Therefore, it is imperative to tackle and study this issue to distinguish the verified information from the unverified ones. There is an increasing interest in rumor detection on microblogs recently, however, it is mostly applied on English language while the work on Arabic language is still ongoing research topic and need more efforts. In this paper, we propose a combined Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to detect rumors on Twitter dataset. Various experiments were conducted to choose the best hyper-parameters tuning to achieve the best results. Moreover, different neural network models are used to evaluate performance and compare results. Experiments show that the CNN-LSTM model achieved the best accuracy 0.95 and an F1-score of 0.94 which outperform the state-of-the-art methods.

• International Journal of Knowledge Content Development & Technology
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• v.12 no.4
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• pp.41-65
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• 2022

This study would understand the overall perception of our society about public libraries, analyzing the texts related to public libraries, utilizing the semantic connection network & sentiment analysis. For this purpose, this study collected data from the last five years with keywords, 'Library' and 'Lifelong Learning Center' from January 1, 2016 through November 30, 2020 through the blogs and cafés of major domestic portal sites. With the collected data, text mining, centrality of keywords, network structure, structural equipotentiality, and sensitivity analyses were conducted. As a result of the analysis, First, 'reading' and 'book' were identified as representative keywords that form the social perception of public libraries. Second, it turned out that there were keywords related to the use of the library and the untact service due to the recent spread of COVID-19. Third, in seeking a plan for the development of public libraries through the keywords drawn to have positive meanings, it is necessary to create continuous services that can form a new image of the library, breaking away from the existing fixed role and image of the library and increase the convenience of use. Fourth, facilities and facilities for library services were recognized from a neutral point of view. Fifth, the spread of infectious diseases, social distancing, and temporary closure and closure of libraries are negatively related to public libraries, and awareness of librarians has been identified as negative keywords.

• International journal of advanced smart convergence
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• v.11 no.2
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• pp.163-170
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• 2022

Nursing students who play a role as future nursing professions are provided with education through various teaching and learning methods in order to develop necessary competencies. The purpose of this study is to confirm the effect of nursing practice education using multimedia. A quasi experimental study with a nonequivalent control group pretest-posttest design was used, and the participants of the study were students from two nursing colleges, who received an educational intervention using multimedia as the experimental group and those without education were selected as the control group. Data collection was conducted immediately before and after educational intervention, and data analysis was performed using the SPSS 21.0 program by x²-test, Fisher's exact probability, and t-test. As a result of the study, the experimental group was statistically significant in self-efficacy (t=3.402, p=0.015), resilience (t=2.047, p=0.045) and performance confidence (t=2.128, p=0.018) compared to the control group. Through these results, we could confirm that multi-media practical education is effective educational method for enhancing nursing students' self-efficacy, resilience, and performance confidence. Therefore, in order to establish a systematization of the nursing profession, it is essential and should be continued for nursing students to use structured multimedia and core fundamental nursing skills.

• Educational Technology International
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• v.23 no.1
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• pp.67-96
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• 2022

Although blogs have been used in online learning environments with optimistic expectations, the distributed nature of blogs can pose some challenges. Currently, we do not have a robust collection of tested blogging strategies to help students interact more effectively with each other when blogs are used as a primary form of engagement in an online class. Thus, the purpose of the study was to test an early iteration of an instructional blogging strategy, "Featured Student Profiles," which is designed to help students become acquainted with each other better and encourage them to visit and comment on each other's blogs. Sixteen pre-service teachers who were enrolled in an online course in which student blogs are the primary medium of peer interactions, participated in the study. Using a design case approach, seven students participated in interviews and all student blog interactions were analyzed. Thematic analysis was applied to analyze the interview data and identify salient themes of students' blogging experiences overall under the study strategy. The findings indicated that students took the most direct and efficient path they experienced to complete the blog task. Their peer interaction patterns varied, but several shifted from random to targeted relationships as the semester progressed. Although all students perceived the strategy as a positive approach to peer awareness, there was no clear evidence of its effect on student interactions.

• Journal of Broadcast Engineering
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• v.28 no.2
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• pp.230-237
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• 2023

Self-driving is a key technology of the fourth industry and can be applied to various places such as cars, drones, cars, and robots. Among them, localiztion is one of the key technologies for implementing autonomous driving as a technology that identifies the location of objects or users using GPS, sensors, and maps. Locilization can be made using GPS or LIDAR, but it is very expensive and heavy equipment must be mounted, and precise location estimation is difficult for places with radio interference such as underground or tunnels. In this paper, to compensate for this, we proposes an image retrieval using attention module and image segmentation maps using color images acquired with low-cost vision cameras as an input.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.11a
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• pp.203-206
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• 2022

본 논문에서는 생성된 이미지에 대한 YOLO 모델의 객체 인식의 성능을 확인하고 사례를 연구하는 것을 목적으로 한다. 최근 영상 처리 기술이 발전함에 따라 적대적 공격의 위험성이 증가하고, 이로 인해 객체 인식의 성능이 현저히 떨어질 수 있는 문제가 발생하고 있다. 본 연구에서는 앞서 언급한 문제를 해결하기 위해 text-to-image 모델을 활용하여 기존에 존재하지 않는 새로운 이미지를 생성하고, 생성된 이미지에 대한 객체 인식을 사례 별로 연구한다. 총 8가지의 동물 카테고리로 분류한 후 객체 인식 성능을 확인한 결과 86.46%의 정확도로 바운딩 박스를 생성하였고, 동물에 대한 116개의 60.41%의 정확도를 보여주었다.

• Annual Conference of KIPS
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• 2022.05a
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• pp.385-387
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• 2022

With the tremendous rise in popularity of the Internet and technological advancements, many news keeps generating every day from multiple sources. As a result, the information (News) on the network has been highly increasing. The critical problem is that the volume of articles or news content can be overloaded for the readers. Therefore, the people interested in reading news might find it difficult to decide which content they should choose. Recommendation systems have been known as filtering systems that assist people and give a list of suggestions based on their preferences. This paper studies a personalized news recommendation system to help users find the right, relevant content and suggest news that readers might be interested in. The proposed system aims to build a hybrid system that combines collaborative filtering with content-based filtering to make a system more effective and solve a cold-start problem. Twitter social media data will analyze and build a user's profile. Based on users' tweets, we can know users' interests and recommend personalized news articles that users would share on Twitter.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.67-70
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• 2022

최근 딥러닝 방법의 발전하면서 영상처리 및 컴퓨터 비전의 다양한 분야에서 딥러닝 기반의 알고리즘들이 그 이전의 방법들에 비하여 큰 성능 향상을 보이고 있다. 손실 영상 압축의 경우 최근 encoder-decoder 형태의 네트웍이 영상 압축에서 사용되는 transform을 대체하고 있고, transform 결과들의 엔트로피 코딩을 위한 추가적인 encoder-decoder 네트웍을 사용하여 HEVC 수준에 버금가는 성능을 내고 있다. 무손실 압축의 경우에도 매 픽셀 예측을 CNN으로 수행하는 경우, 기존의 예측방법들에 비하여 예측성능이 크게 향상되어 JPEG-2000 Lossless, FLIF, JEPG-XL 등의 딥러닝을 사용하지 않는 방법들에 비하여 우수한 성능을 내는 것으로 보고되고 있다. 그러나 모든 픽셀에 대하여 예측값을 CNN을 통하여 계산하는 방법은, 영상의 픽셀 수 만큼 CNN을 수행해야 하므로 HD 크기 영상에 대하여 지금까지 알려진 가장 빠른 방법이 한 시간 이상 소요되는 등 비현실적인 것으로 알려져 있다. 따라서 최근에는 성능은 이보다 떨어지지만 속도를 현실적으로 줄인 방법들이 제안되고 있다. 이러한 방법들은 초기에는 FLIF나 JPEG-XL에 비하여 성능이 떨어져서, GPU를 사용하면서도 기존의 방법보다 좋지 않은 성능을 보인다는 면에서 여전히 비현실적이었다. 최근에는 신호의 특성을 더 잘 활용하는 방법들이 제안되면서 매 픽셀마다 CNN을 수행하는 방법보다는 성능이 떨어지지만, 짧은 시간 내에 FLIF나 JPEG-XL보다는 좋은 성능을 내는 현실적인 방법들이 제안되었다. 본 연구에서는 이러한 최근의 몇 가지 방법들을 살펴보고 이들보다 성능을 더 좋게 할 수 있는 보조적인 방법들과 raw image에 대한 성능을 평가한다.

• Annual Conference of KIPS
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• 2011.04a
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• pp.1588-1591
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• 2011

통합교육의 실시와 다문화 가정의 자녀 증가 등으로 학교 교실의 구성원들이 다양화 되고 있다. 그러나 제한적인 매체와 전통적인 교육과정, 교수방법 및 평가 등의 사용으로 학습자들의 다양성을 고려되지 못하고 있다. 본 연구는 디지털화 교수매체의 활용함으로써 다양한 학습자들의 정보에 대한 접근성을 보장해 주고 나아가 교육현장에서 보편적 학습설계(Universal Design for Learning: UDL)를 적용하여 학습자의 다양성을 보장하는데 그 목적을 둔다. 교육현장에 UDL 적용에 대한 예비교사와 현직교사의 그룹 토론을 통해, UDL의 적용가능성과 적용함에 있어서 어려움을 알아보았다. 그 결과, 정해진 교육과정과 교재, 다양한 학습자에 대한 교사의 전문성 부족, 학생들 간의 학습 차이, 학급당 많은 학생수, 교사의 많은 업무량 등 여러 가지 문제점으로 UDL 적용이 어렵다고 하였다. 해결방안으로 교수설계에 UDL의 적용과 다양한 디지털화 교수매체의 활용으로 접근성 보장을 제안하였다. 교육현장에서의 UDL 확대와 다양한 학습자들의 접근성이 보장된 교육을 할 수 있도록 교사들이 수업에서의 UDL 적용 방안에 대해 제시하였다.