• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• 제30권4호
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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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• 제20권2호
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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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• 제20권1호
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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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• 제14권8호
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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.

• 방송공학회논문지
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• 제26권4호
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• pp.419-428
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• 2021

그림자 제거는 객체 추적 및 검출 등 영상처리 기술의 핵심 전처리 요소이다. 최근 심층 합성곱 신경망 (Deep Convolutional Neural Network) 기반의 영상 인식 기술이 발전함에 따라 심층 학습을 이용한 그림자 제거 연구들이 활발히 진행되고 있다. 본 논문에서는 자기 주의 증류(Self Attention Distillation)를 이용하여 심층 특징을 추출하는 새로운 그림자 제거 방법을 제안한다. 제안된 방법은 각 층에서 추출된 그림자 검출 결과를 하향식 증류를 통해 점진적으로 정제한다. 특히, 그림자 검출 결과에 대한 정답을 이용하지 않고 그림자 제거를 위한 문맥적 정보를 형성함으로써 효율적인 심층 신경망 학습을 수행한다. 그림자 제거를 위한 다양한 데이터 셋에 대한 실험 결과를 통해 제안하는 방법이 실제 환경에서 발생한 그림자 제거에 효과적임을 보인다.

• 한국정보통신학회논문지
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• 제25권7호
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• pp.869-876
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• 2021

음악 분야에서는 최근 머신러닝을 이용한 다양한 인공지능 작곡 방법이 시도되고 있다. 하지만 이 연구는 대부분 서양음악을 중심으로 이루어져왔고 국악에 관한 연구는 거의 이루어지지 않았다. 특히 연구를 위한 데이터 세트조차 만들어지지 않은 상태여서 연구에 어려움이 많았다. 이에 해당 논문에서는 국악의 데이터 세트를 만들고 그 데이터 세트를 기반으로 하여 세 가지 알고리즘을 이용하여 국악 멜로디를 생성하고 그 결과물을 비교하여 보기로 한다. 언어와 음악의 유사성에 기반한 LSTM, Music Transformer 그리고 Self Attention 3가지 모델들이 선택되었다. 각 3가지 모델을 이용하여 국악 멜로디 생성기를 모델링하고 학습시켜 국악 멜로디를 생성해 내었다. 사용자 평가 결과 Self Attention 방식이 LSTM 방식과 Music transformer 방식에 비해 높은 선호도를 보였다. 데이터 표현 및 훈련데이터는 인공지능 작곡에 있어 매우 중요하다. 이를 위한 기초적인 국악 데이터 세트를 만들고 다양한 알고리즘으로 인공지능 작곡을 시도하였고 이것이 향후 국악 인공지능 작곡의 연구에 도움이 될 수 있을 것으로 기대한다.

• 복식문화연구
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• 제22권2호
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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.

• 방송공학회논문지
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• 제27권4호
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• pp.538-547
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• 2022

최근 소개된 트랜스포머(Transformer)를 이용한 이미지 분류 방법들은 기존 합성곱 신경망 기반 방법 대비 괄목할 만한 성능 향상을 보여주고 있다. 지역적 특성을 효과적으로 고려하기 위해 이미지 영역을 복수의 윈도우 영역으로 나누어 트랜스포머를 적용하는 방법에 대한 연구가 활발히 진행되어 왔으나, 윈도우 간 관계 및 중요도에 대한 학습은 여전히 부족한 상황이다. 본 논문에서는 이러한 문제점을 극복하기 위해 각 윈도우의 중요도를 학습에 반영할 수 있는 트랜스포머 구조를 제안한다. 제안하는 방법은 각 윈도우 영역에 대한 자기주의(Self-attention) 연산을 기반으로 압축과 완전 연결 계층(Fully Connected Layer)을 통해 각 윈도우 영역의 중요도를 계산한다. 계산된 중요도는 윈도우 영역들 간의 관계를 학습한 가중치로써 각 윈도우 영역에 곱해져 특징 값을 재조정 한다. 실험 결과를 통해 제안하는 방법이 기존 트랜스포머 기반 방법의 성능을 효과적으로 향상 시킬 수 있음을 보인다.

• 로봇학회논문지
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• 제17권4호
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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.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 춘계학술발표대회
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• pp.485-487
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• 2023

컴퓨터 비전의 하위 태스크(Task)인 의미론적 분할(Semantic Segmentation)은 자율주행, 해상에서 선박찾기 등 다양한 분야에서 연구되고 있다. 기존 FCN(Fully Conovlutional Networks) 기반 의미론적 분할 모델은 다운샘플링(Dowsnsampling)과정에서 공간정보의 손실이 발생하여 정확도가 하락했다. 본 논문에서는 공간정보 손실을 완화하고자 PSA(Polarized Self-attention)의 공간정보 강조 모듈을 HRNet(High-resolution Networks)의 합성곱 블록 사이에 추가한다. 실험결과 파라미터는 3.1M, GFLOPs는 3.2G 증가했으나 mIoU는 0.26% 증가했다. 공간정보가 의미론적 분할 정확도에 영향이 미치는 것을 확인했다.