• International journal of advanced smart convergence
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• v.4 no.2
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• pp.20-28
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• 2015

In this paper, we propose an effective tracking algorithm with an appearance model based on features extracted from a video frame with posture variation and camera view point adaptation by employing the non-adaptive random projections that preserve the structure of the image feature space of objects. The existing online tracking algorithms update models with features from recent video frames and the numerous issues remain to be addressed despite on the improvement in tracking. The data-dependent adaptive appearance models often encounter the drift problems because the online algorithms does not get the required amount of data for online learning. So, we propose an effective tracking algorithm with an appearance model based on features extracted from a video frame.

• Korean Journal of Health Education and Promotion
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• v.25 no.4
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• pp.13-28
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• 2008

Objectives: The purpose of this study was to assess general population' AIDS knowledge and attitudes towards AIDS. Methods: Using a multistage area sampling and random digit dialing method, a random sample of individuals aged 19-59 years, living in 16 different area in Korea during September 2007 were interviewed by telephone. Results: In all, 1,087 respondents completed the survey. In most items, respondents displayed a fairly good degree of knowledge about AIDS. However it was lower than that in developed countries. Respondents' attitudes towards AIDS were found to be negative. The knowledge and attitudes towards AIDS were more positive for respondents who were young, educated, office worker or students, higher in income. The higher level of knowledge, the more positive attitudes towards AIDS. Conclusions: In general AIDS related knowledge was relatively low and people showed negative attitudes towards AIDS. Incorrect knowledge and lack of information on AIDS would lead to unnecessary negative attitudes and there need to be addressed by health education programs and campaigns using mass media.

• Journal of Broadcast Engineering
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• v.24 no.1
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• pp.97-104
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• 2019

In the field of video watermarking, which adds a time axis to the watermarking of the existing still image, it is important to embed a watermark at a level that can claim copyright while minimizing the overall video quality. In this paper, we focus on the fact that B slices refer to blocks of I and P slices in the Random Access compression method of HEVC. Therefore, We propose that watermark is embedded only in I and P slices and extracted in I, P, and B slices. This can minimize the decreases of the overall image quality of the image and protect the copyright of the B slice without directly embedding the watermark.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.2938-2956
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• 2020

Objective image quality assessment (IQA) models have been developed by effective features to imitate the characteristics of human visual system (HVS). Actually, HVS is extremely sensitive to color degradation and complex texture changes. In this paper, we firstly reveal that many existing full reference image quality assessment (FR-IQA) methods can hardly measure the image quality with contrast and masking texture changes. To solve this problem, considering texture masking effect, we proposed a novel FR-IQA method, called Texture and Color Quality Index (TCQI). The proposed method considers both in the masking effect texture and color visual perceptual threshold, which adopts three kinds of features to reflect masking texture, color difference and structural information. Furthermore, random forest (RF) is used to address the drawbacks of existing pooling technologies. Compared with other traditional learning-based tools (support vector regression and neural network), RF can achieve the better prediction performance. Experiments conducted on five large-scale databases demonstrate that our approach is highly consistent with subjective perception, outperforms twelve the state-of-the-art IQA models in terms of prediction accuracy and keeps a moderate computational complexity. The cross database validation also validates our approach achieves the ability to maintain high robustness.

• Journal of Digital Convergence
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• v.17 no.4
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• pp.365-379
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• 2019

The purpose of this study is to investigate what factors affect cyber-delinquency after examining the previous research focusing on the general strain theory and the delinquency opportunity theory in the existing studies. And as adolescents move from middle school to high school, this study is intended to analyze what factors affect cyber-delinquency from a longitudinal perspective using KCYPS(Korea Child and Youth Panel Survey) elementary 4th grade fourth and seventh data. The adolescence cyber-delinquency probability of occurrence were analyzed through the panel logit fixed-effect model using STATA. And then the cyber-delinquency frequency of adolescents were analyzed through the panel tobit random-effect model. As a result of analyzing the factors affecting cyber-delinquency frequency, Adult media commitment, computer use time, and cell phone dependency increased cyber-delinquency frequency. On the other hand, among the parenting attitudes, the attitude of supervising attentively and adolescents' age-increasing decreased cyber-delinquency frequency.

• The korean journal of orthodontics
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• v.53 no.5
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• pp.328-335
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• 2023

Objective: The aim of this study was to determine whether an association between study quality, other study characteristics, and Altmetric Attention Scores (AASs) existed in orthodontic studies. Methods: The Scopus database was searched to identify orthodontic studies published between January 1, 2017, and December 31, 2019. Articles that satisfied the eligibility criteria were included in this study. Study characteristics, including study quality were extracted and entered into a pre-pilot data collection sheet. Descriptive statistics were calculated. On an exploratory basis, random forest and gradient boosting machine learning algorithms were used to examine the influence of article characteristics on AAS. Results: In total, 586 studies with an AAS were analyzed. Overall, the mean AAS of the samples was 5. Twitter was the most popular social media platform for publicizing studies, accounting for 53.7%. In terms of study quality, only 19.1% of the studies were rated as having a high level of quality, with 41.8% of the studies deemed moderate quality. The type of social media platform, number of citations, impact factor, and study type were among the most influential characteristics of AAS in both models. In contrast, study quality was one of the least influential characteristics on the AAS. Conclusions: Social media platforms contributed the most to the AAS for orthodontic studies, whereas study quality had little impact on the AAS.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.67-80
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• 2023

Rice is an important food crop for most of the population in Nowadays, psychologists consider social media an important tool to examine mental disorders. Among these disorders, depression is one of the most common yet least cured disease Since abundant of writers having extensive followers express their feelings on social media and depression is significantly increasing, thus, exploring the literary text shared on social media may provide multidimensional features of depressive behaviors: (1) Background: Several studies observed that depressive data contains certain language styles and self-expressing pronouns, but current study provides the evidence that posts appearing with self-expressing pronouns and depressive language styles contain high emotional temperatures. Therefore, the main objective of this study is to examine the literary cyber writers' posts for discovering the symptomatic signs of depression. For this purpose, our research emphases on extracting the data from writers' public social media pages, blogs, and communities; (3) Results: To examine the emotional temperatures and sentences usage between depressive and not depressive groups, we employed the SentiStrength algorithm as a psycholinguistic method, TF-IDF and N-Gram for ranked phrases extraction, and Latent Dirichlet Allocation for topic modelling of the extracted phrases. The results unearth the strong connection between depression and negative emotional temperatures in writer's posts. Moreover, we used Naïve Bayes, Support Vector Machines, Random Forest, and Decision Tree algorithms to validate the classification of depressive and not depressive in terms of sentences, phrases and topics. The results reveal that comparing with others, Support Vectors Machines algorithm validates the classification while attaining highest 79% f-score; (4) Conclusions: Experimental results show that the proposed system outperformed for detection of depression trends in literary cyber writers using sentiment analysis.

• Smart Media Journal
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• v.12 no.1
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• pp.9-16
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• 2023

Recently, a convolutional neural network (CNN) based system is being developed to overcome the limitations of human resources in the apple quality classification of farmhouse. However, since convolutional neural networks receive only images of the same size, preprocessing such as sampling may be required, and in the case of oversampling, information loss of the original image such as image quality degradation and blurring occurs. In this paper, in order to minimize the above problem, to generate a image patch based graph of an original image and propose a random walk-based positional encoding method to apply the graph transformer model. The above method continuously learns the position embedding information of patches which don't have a positional information based on the random walk algorithm, and finds the optimal graph structure by aggregating useful node information through the self-attention technique of graph transformer model. Therefore, it is robust and shows good performance even in a new graph structure of random node order and an arbitrary graph structure according to the location of an object in an image. As a result, when experimented with 5 apple quality datasets, the learning accuracy was higher than other GNN models by a minimum of 1.3% to a maximum of 4.7%, and the number of parameters was 3.59M, which was about 15% less than the 23.52M of the ResNet18 model. Therefore, it shows fast reasoning speed according to the reduction of the amount of computation and proves the effect.

• Korean Journal of Optics and Photonics
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• v.14 no.4
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• pp.429-433
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• 2003

We try to analyse the localization phenomenon of a lightwave in random media by means of considering the solution of the propagation equation on a transmission line in which the propagation constants are randomly distributed. Lightwave localization is generated at the turning point where the solution is changed suddenly from an increase to a decrease. First, in order to investigate the changing process of the solution, we have derived the approximated one-dimensional Schrodinger equation from the two-dimensional wave equation by using the Brags condition. Considering the many types of solutions of the wave equation, we have investigated the conditions that allow the solutions to exist. Also, we have investigated the relationships between the localization of the solution and the variation of the propagation constant. In case of the exponential solution, we know that the permittivity $\varepsilon$=(0,0$\varepsilon$$_{0}$) is a very important parameter to influence the phase of the lightwave and to generate the localization.

• Journal of Broadcast Engineering
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• v.25 no.2
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• pp.176-184
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• 2020

Due to constant development and decline on Seoul areas the Seoul government is pushing various policies to regenerate declined Seoul areas. Theses various policies lead to land-use changes around numerous Seoul districts. This study aims to create prediction model which can foresee future land-use changes and while doing so, tried to derive various influential factors which leads to land-use changes. To do so, various open-data from national departments and Seoul government have been collected and implemented into random forest algorithm. The results showed promising accuracy and derived multiple influential factors which causes land-use changes around Seoul districts. The result of this study could further be implemented in policy makings for the public sectors, or could also be used as basis for studying gentrification problems happening in Seoul Area.