• 조명전기설비학회논문지
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• 제27권5호
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• pp.9-17
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• 2013

Preferred combinations of illuminance and color temperature of lighting depend on daily living activities. We investigated whether the illumination stimuli of LED lighting can enhance attention and relaxation level by controlling color temperature and illuminance level according to activities. Illuminations and color temperatures of LED flat panels are controlled in accordance with activities such as office work and resting. The attention and relaxation level under the task specific lightings are compared with those under normal lighting condition. Single channel EEG signals from the NeuroSky's Mindset are used to estimate attention and relaxation level of human subjects under different lighting conditions. Experiment results show that high color temperature with high illuminance of LED lightings (6600K, 800lx) shows improved attention level compared with conventional lighting conditions (4000K, 500lx).

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권12호
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• pp.3976-3990
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• 2022

Currently, cross-attention is widely used in Siamese trackers to replace traditional correlation operations for feature fusion between template and search region. The former can establish a similar relationship between the target and the search region better than the latter for robust visual object tracking. But existing trackers using cross-attention only focus on rich semantic information of high-level features, while ignoring the appearance information contained in low-level features, which makes trackers vulnerable to interference from similar objects. In this paper, we propose a Multi-level Cross-attention Siamese network(MCSiam) to aggregate the semantic information and appearance information at the same time. Specifically, a multi-level cross-attention module is designed to fuse the multi-layer features extracted from the backbone, which integrate different levels of the template and search region features, so that the rich appearance information and semantic information can be used to carry out the tracking task simultaneously. In addition, before cross-attention, a target-aware module is introduced to enhance the target feature and alleviate interference, which makes the multi-level cross-attention module more efficient to fuse the information of the target and the search region. We test the MCSiam on four tracking benchmarks and the result show that the proposed tracker achieves comparable performance to the state-of-the-art trackers.

• 디지털융복합연구
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• 제11권10호
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• pp.731-736
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• 2013

아동들의 주의력 결핍으로 인한 여러 가지 사회적 문제가 야기되고 있다. 주의력 결핍이 심각한 아동들을 위한 아동 친화적인 주의력 향상 방법이 제공될 필요성이 있다. 최근 아동들은 3D 게임을 매우 좋아하며 자발적인 참여를 하고 있다. 그러므로 아동들이 좋아하는 3D 게임을 이용하여 아동들의 주의력을 향상시키고자 3D 게임을 통한 주의력을 측정하는 방법에 대한 연구를 수행하였다. 기존 의학계에서 사용하는 주의력 측정 방법들을 고찰하고 게임내에서 주의력을 측정할 수 있는 변수들을 설정하였다. 누락 오류, 오경보 오류, 정반응 시간 평균, 정반응 시간 표준편차가 설정된 변수들인데, 일반아동과 주의력이 부족한 아동간에 대하여 시각 주의력에 대한 실험을 통해, 이들 변수들 간에 값의 차이가 있음을 알 수 있었다.

• 한국식생활문화학회지
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• 제29권5호
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• pp.464-475
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• 2014

Eating breakfast provides crucial nutrition for brain function and helps promote overall health. It is especially critical in growing adolescents, as it is known to form good eating habits and better study habits. This study investigated the effects of skipping breakfast on nutritional state, fatigue level, and attention level. A cross-sectional study was conducted in 2010 on total of 828 adolescents composed of 414 boys and 414 girls. Students who ate breakfast never to twice per week were placed in the breakfast-skipper group while students who ate breakfast more than five times per week were included in the breakfast-eater group. Students performed a self-reported questionnaire on food behaviors, amount of food consumption, fatigue level, attention deficient hyperactivity disease (ADHD) level by Conners-Wells' Adolescent Self-Report Scales, depression scale, and self-esteem level. Statistical analysis was conducted using the SAS program (version 9.1). A total of 135 boys (32.6%) and 138 girls (33.3%) were included in the breakfast-skipper group, whereas 241 boys (58.2%) and 223 girls (53.9%) were included in the breakfast-eater group. The breakfast-skipper group showed irregular food behaviors and lacked nutrients. Specifically, energy (p< .001), protein (p< .001), dietary fiber (p< .001), calcium (p< .01), vitamin A (p< .01), thiamin (p< .05), niacin (p< .001) levels in boy breakfast-skippers were statistically lower compared to boy breakfast-eaters. Intakes of all nutrients except fat in girl breakfast-skippers were statistically lower than in girl breakfast-eaters. Girl breakfast-skippers (41.3%) showed significantly higher fatigue risks compared to girl breakfast-eaters (21.5%). Low attention level was also observed only in girls in the breakfast-skipping group. Moreover, students in the breakfast-skipper group showed higher scores for depression and low self-esteem (p< .001). In conclusion, skipping breakfast has effects on young adolescents' nutrition, manifesting as high fatigue level and low attention level, especially in girls.

• 한국정보전자통신기술학회논문지
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• 제16권5호
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• pp.301-306
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• 2023

Attention is a fundamental aspect in the cognitive process of human. Cognitive system of human body requires to focus on selected information among a vast amount of information from sensory organs. It has widely studied that various environmental factors affected the level of attention; however, few researches have aimed to the effect of direct cryotherapy. In this research, level of attention was studied comparing sub-indexes of FAIR test between groups with different duration of direct cryotheapy to the back of neck. FAIR test is a evaluation tool for visual attention consisting of three sub-indexes. Selective attention, accuracy of attention, and persistence of attention can be independently analyzed by FAIR test. In the analysis of selective attention, cryotherapy for 5 to 20 minutes showed higher result than cryotherapy for 40 minutes. In the analysis of persistence of attention, cryotherapy for 5 to 15 minutes showed higher result than cryotherapy for 40 minutes. Overall, selective attention and persistence of attention turns out to be maximized between 5 to 20 minutes of cryotherapy and tends to decrease afterwards. However, accuracy of attention does not seem to be affected by the duration of cryotherapy. Correlation between selective attention and the skin temperature by cryotherapy tends to be negative supporting the findings by ANOVA and post-hoc test. Correlation between persistence of attention and the skin temperature showed similar results.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2019년도 추계학술발표대회
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• pp.958-961
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• 2019

Convolutional neural network(CNN)의 등장으로 얼굴 표현 인식 연구는 많은 발전을 이루었다. 그러나, 기존의 CNN 접근법은 미리 학습된 훈련모델에서 Multiple-level 의 의미적 맥락을 포함하지 않는 Attention-embedded 문제가 발생한다. 사람의 얼굴 감정은 다양한 근육의 움직임과 결합에 기초하여 관찰되며, CNN 에서 딥 레이어의 산출물로 나온 특징들의 결합은 많은 서브샘플링 단계를 통해서 class 구별와 같은 의미 정보의 손실이 일어나기 때문에 전이 학습을 통한 올바른 훈련 모델 생성이 어렵다는 단점이 있다. 따라서, 본 논문은 Backbone 네트워크의 Multi-level 특성에서 Channel-wise Attention 통합 및 의미 정보를 포함하여 높은 인식 성능을 달성하는 Densely Backwarnd Attention(DBA) CNN 방법을 제안한다. 제안하는 기법은 High-level 기능에서 채널 간 시멘틱 정보를 활용하여 세분화된 시멘틱 정보를 Low-level 버전에서 다시 재조정한다. 그런 다음, 중요한 얼굴 표정의 묘사를 분명하게 포함시키기 위해서 multi-level 데이터를 통합하는 단계를 추가로 실행한다. 실험을 통해, 제안된 접근방법이 정확도 79.37%를 달성 하여 제안 기술이 효율성이 있음을 증명하였다.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 2018년도 제30회 한글 및 한국어 정보처리 학술대회
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• pp.259-262
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• 2018

기계독해의 목표는 기계가 주어진 문맥을 이해하고 문맥에 대한 질문에 대답할 수 있도록 하는 것이다. 본 논문에서는 Multi-level Attention에 정보를 효율적으로 융합 수 있는 Fusion 함수를 결합하고, Answer module에Stochastic multi-step answer를 적용하여 SQuAD dev 데이터 셋에서 EM=78.63%, F1=86.36%의 성능을 보였다.

• 스마트미디어저널
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• 제12권9호
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• pp.45-59
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• 2023

Weakly supervised object localization (WSOL) is a task of localizing an object in an image using only image-level labels. Previous studies have followed the conventional class activation mapping (CAM) pipeline. However, we reveal the current CAM approach suffers from problems which cause original CAM could not capture the complete defects features. This work utilizes a convolutional neural network (CNN) pretrained on image-level labels to generate class activation maps in a multi-scale manner to highlight discriminative regions. Additionally, a vision transformer (ViT) pretrained was treated to produce multi-head attention maps as an auxiliary detector. By integrating the CNN-based CAMs and attention maps, our approach localizes defective regions without requiring bounding box or pixel-level supervision during training. We evaluate our approach on a dataset of apple images with only image-level labels of defect categories. Experiments demonstrate our proposed method aligns with several Object Detection models performance, hold a promise for improving localization.

• 한국산학기술학회논문지
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• 제16권1호
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• pp.429-437
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• 2015

본 연구는 대학생의 스마트폰 중독 경향에 따른 신체활동량, 수면, 주의력 조절 및 자기조절학습과의 차이와 관계를 파악하기 위한 서술적 조사연구이다. 자료수집은 269명의 대학생을 대상으로 구조화된 설문지를 통해 조사하였고 SPSS 18.0 프로그램을 통해 자료를 분석하였다. 연구결과 대상자의 스마트폰 중독수준은 성별에 따라 차이가 있었고, 스마트폰 중독성향이 강할수록 성적과 자가통제력은 낮고, 스마트폰 사용 시간이 길었다. 스마트폰 중독수준과 신체활동량, 수면의 질 및 주의력 조절능력이 유의한 차이가 있었고, 스마트폰 중독정도가 높을수록 신체활동량과 자기조절학습능력 및 수면의 질이 낮은 경향이 있었고, 주의력 조절은 높게 나타나는 경향을 보였다. 이상의 결과를 통해 대학생의 스마트폰의 과다사용으로 일상적 건강과 학습능력이 저하될 수 있으며 이를 방지하기 위한 다양한 차원에서의 스마트폰 중독예방 전략이 필요함을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권4호
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• pp.1275-1292
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• 2021

As a fine-grained classification problem, aspect-level sentiment classification predicts the sentiment polarity for different aspects in context. To address this issue, researchers have widely used attention mechanisms to abstract the relationship between context and aspects. Still, it is difficult to effectively obtain a more profound semantic representation, and the strong correlation between local context features and the aspect-based sentiment is rarely considered. In this paper, a hybrid attention capsule network for aspect-level sentiment classification (ABASCap) was proposed. In this model, the multi-head self-attention was improved, and a context mask mechanism based on adjustable context window was proposed, so as to effectively obtain the internal association between aspects and context. Moreover, the dynamic routing algorithm and activation function in capsule network were optimized to meet the task requirements. Finally, sufficient experiments were conducted on three benchmark datasets in different domains. Compared with other baseline models, ABASCap achieved better classification results, and outperformed the state-of-the-art methods in this task after incorporating pre-training BERT.