• Journal of the Korean Society of School Health
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• v.23 no.2
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• pp.237-245
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• 2010

Purpose: The purpose of this thesis was to examine the effects of social skills training program onto the children with tendency of attention-deficit hyperactivity disorder. Methods: This study used nonequivalent control group pre/post-test quasi-experimental research design. The subjects were 18 children with tendency of attention- deficit hyperactivity in D City. The subjects were divided into two groups, an experimental group of 8 children and a control group of 10. The program consisted of 20 sessions of 60 minutes per session, 5 days a weeks, for 4 weeks. The research tools included Conner's Teacher Rating Scales (CTRS) and Social Skills Rating System (SSRS). The collected data were analyzed using $x^2$ test, Mann-Whitney test on the SPSS 17.0 program. Results: a) the scores for cooperation, self-assertiveness, self-control and empathy increased significantly in the experimental group, compared to the control group. b) the scores for social skills increased significantly in the experimental group, compared to the control group. Conclusion: It appears that the social skills training program is a useful nursing intervention to improve the social skills for children with tendency of attention-deficit hyperactivity.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.30 no.4
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• pp.161-167
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• 2019

Objectives: This study investigated quality of life in Korean juvenile inmates with attention-deficit/hyperactivity disorder (ADHD) and the impact of behavioral and emotional problems on quality of life. Methods: In total, 200 inmates were evaluated using the Korean version of the Mini-International Neuropsychiatric Interview (K-MINI) and the Korean version of the Kiddie-Schedule for Affective Disorders and Schizophrenia-Present and Lifetime (K-SADS-PL-K). We extracted the inmates with ADHD and evaluated their quality of life, behavioral problems, and emotional problems with the Pediatric Quality of Life Inventory (PedsQL) and the Korean Youth Self Report (K-YSR) scale. Descriptive statistics, Pearson correlation analysis, and multiple regression analysis were conducted. Results: Among the 200 total inmates, 68 were diagnosed with ADHD by the K-SADS-PL-K. Most of the correlations between PedsQL scores and K-YSR items were significant. Multiple regression analysis showed that PedsQL could be predicted by affective problems (among the DSM-oriented scales of the K-YSR) and attention problems (among the syndrome scales of the K-YSR). Conclusion: Our results demonstrate that, among juvenile inmates with ADHD, quality of life was negatively correlated with most behavioral and emotional problems. Meanwhile, the significant influence of affective and attention problems on inmates' quality of life suggests the necessity of comprehensive treatments for this group.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.20 no.2
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• pp.82-89
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• 2009

Objectives: Children with attention-deficit hyperactivity disorder(ADHD) often have difficulties in social behavior. The aim of this study was to evaluate the effectiveness of a short-term training program for improving social skills, self-perception and attention deficits. Methods: The subjects were nine children diagnosed with ADHD with(or without) other mental disorders using the Diagnostic Interview Schedule for Children(DISC-ADHD) module. Children were given eight sessions of a social skills training program. Parents of children simultaneously participated in their own training which was designed to support their children's generalization of skills. Assessments included child, parent and teacher ratings of social skills, self-perception and attention deficit at baseline and post-treatment. Results: Social skills training led to significant improvements in child-reported measures of self-esteem, in teacher reported measures of social skills, and in parent-reported measures of attention deficit. Conclusion: This study suggests that short-term social skills training programs for children with ADHD may improve their social skills, self-perception and attention deficits.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.20 no.1
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• pp.23-28
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• 2009

Objectives : This study was conducted to investigate the association between the symptoms of boys with attention-deficit hyperactivity disorder (ADHD) and the attention-deficit hyperactivity symptoms, temperament and character patterns of their parents. Methods : Forty-five boys with ADHD and who met the DSM-IV criteria were evaluated by using the ADHD rating scale (ADHD-RS), and their parents completed the Korean Adult ADHD scale (K-AADHDS) and the Temperament and Character Inventory (TCI). Results : The parental K-AADHDS scores were not associated with the ADHD-RS total score and the subscale scores of their siblings. The most potent variable related to the ADHD-RS total score was the maternal self-directedness, and the second was the maternal persistence. The maternal self-directedness was the variable that was most correlated with the hyperactivity/impulsivity subscale scores of the ADHD-RS. Conclusion : The results suggest that the paternal ADHD symptoms may not be related to the ADHD symptoms of boys with ADHD. Higher maternal self-directedness and persistence may decrease overall the ADHD symptoms of these boys, and higher maternal self-directedness itself may predict lower hyperactivity/impulsivity symptoms of the boys with ADHD.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3168-3186
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• 2020

Automatic topic essay generation (TEG) is a controllable text generation task that aims to generate informative, diverse, and topic-consistent essays based on multiple topics. To make the generated essays of high quality, a reasonable method should consider both diversity and topic-consistency. Another essential issue is the intrinsic link of the topics, which contributes to making the essays closely surround the semantics of provided topics. However, it remains challenging for TEG to fill the semantic gap between source topic words and target output, and a more powerful model is needed to capture the semantics of given topics. To this end, we propose a pretraining-based contextual self-attention (PC-SAN) model that is built upon the seq2seq framework. For the encoder of our model, we employ a dynamic weight sum of layers from BERT to fully utilize the semantics of topics, which is of great help to fill the gap and improve the quality of the generated essays. In the decoding phase, we also transform the target-side contextual history information into the query layers to alleviate the lack of context in typical self-attention networks (SANs). Experimental results on large-scale paragraph-level Chinese corpora verify that our model is capable of generating diverse, topic-consistent text and essentially makes improvements as compare to strong baselines. Furthermore, extensive analysis validates the effectiveness of contextual embeddings from BERT and contextual history information in SANs.

• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.419-428
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• 2021

Shadow removal plays a key role for the pre-processing of image processing techniques such as object tracking and detection. With the advances of image recognition based on deep convolution neural networks, researches for shadow removal have been actively conducted. In this paper, we propose a novel method for shadow removal, which utilizes self attention distillation to extract semantic features. The proposed method gradually refines results of shadow detection, which are extracted from each layer of the proposed network, via top-down distillation. Specifically, the training procedure can be efficiently performed by learning the contextual information for shadow removal without shadow masks. Experimental results on various datasets show the effectiveness of the proposed method for shadow removal under real world environments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.7
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• pp.869-876
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• 2021

In the field of music, various AI composition methods using machine learning have recently been attempted. However, most of this research has been centered on Western music, and little research has been done on Korean traditional music. Therefore, in this paper, we will create a data set of Korean traditional music, create a melody using three algorithms based on the data set, and compare the results. Three models were selected based on the similarity between language and music, LSTM, Music Transformer and Self Attention. Using each of the three models, a melody generator was modeled and trained to generate melodies. As a result of user evaluation, the Self Attention method showed higher preference than the other methods. Data set is very important in AI composition. For this, a Korean traditional music data set was created, and AI composition was attempted with various algorithms, and this is expected to be helpful in future research on AI composition for Korean traditional music.

• The Research Journal of the Costume Culture
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• v.22 no.2
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• pp.273-289
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• 2014

The purpose of this study is to identify the determinants of the susceptibility of global consumer culture. As determinants, materialism and self monitoring as psychological variables and fashion clothing product knowledge as clothing-related variable were included. It was hypothesized that both psychological variables and clothing-related variable influence susceptibility of global consumer culture. Data were gathered by surveying university students in Seoul metropolitan area, using convenience sampling, and 311 questionnaires were used in the statistical analysis. In analyzing data, exploratory factor analysis using SPSS and confirmatory factor analysis and path analysis using AMOS were conducted. Factor analysis of susceptibility of global consumer culture revealed four dimensions, 'social prestige' factor, 'quality perception' factor, 'conformity to others' factor, and 'conformity to consumption trend' factor. In addition, factor analysis of self monitoring revealed three dimensions, 'center-oriented attention' factor, 'situation-appropriate self-presentation' factor, and 'strategic displays of self-presentation' factor. The results showed that all the fit indices for the variable measures were quite acceptable. In addition, the overall fit of the model suggests that the model fits the data well. Tests of the hypothesized path show that all variables except for the one factor of self monitoring, 'center-oriented attention', and materialism influence all the factors of susceptibility of global consumer culture. The implications of these findings and suggestions for future study are also discussed.

• Journal of Broadcast Engineering
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• v.27 no.4
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• pp.538-547
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• 2022

Recently introduced image classification methods using Transformers show remarkable performance improvements over conventional neural network-based methods. In order to effectively consider regional features, research has been actively conducted on how to apply transformers by dividing image areas into multiple window areas, but learning of inter-window relationships is still insufficient. In this paper, to overcome this problem, we propose a transformer structure that can reflect the relationship between windows in learning. The proposed method computes the importance of each window region through compression and a fully connected layer based on self-attention operations for each window region. The calculated importance is scaled to each window area as a learned weight of the relationship between the window areas to re-calibrate the feature value. Experimental results show that the proposed method can effectively improve the performance of existing transformer-based methods.

• The Journal of Korea Robotics Society
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• v.17 no.4
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• pp.425-430
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• 2022

As robots are considered one of the mainstream digital transformations, robots with machine vision becomes a main area of study providing the ability to check what robots watch and make decisions based on it. However, it is difficult to find a small object in the image mainly due to the flaw of the most of visual recognition networks. Because visual recognition networks are mostly convolution neural network which usually consider local features. So, we make a model considering not only local feature, but also global feature. In this paper, we propose a detection method of a small marker on the object using deep learning and an algorithm that considers global features by combining Transformer's self-attention technique with a convolutional neural network. We suggest a self-attention model with new definition of Query, Key and Value for model to learn global feature and simplified equation by getting rid of position vector and classification token which cause the model to be heavy and slow. Finally, we show that our model achieves higher mAP than state of the art model YOLOr.