• 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.

• PNF and Movement
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• 제9권1호
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• pp.55-63
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• 2011

Purpose : This study was to investigate the application of Rhythmic Initiation(RI) in the respect of information processing in motor learning. Methods : A computer-aided literature search was performed in PubMed and adapted to the other databases and the others were in published books. The following keywords were used: Rhythmic Initiation, attention, memory, motor accuracy, feedback, motor learning, motor control, PNF, cognition. Results : The characterization of RI is rhythmic motion of limb or body through the desired range, starting with passive motion and progressing to active resisted movement. This study suggested that the relationship between of RI and motor learning through the respect of information processing, memory, attention and motor accuracy. Conclusion : Only Rhythmic Initiation, specifically focused on the effects of information processing approach, suggesting that RI can be positively influeced on sensory-perception, attention, memory, motor accuracy. however, it is unclear whether positive effects in the laboratory and field can be generalized to improve. In addition, sustainability of motor learning with RI remains uncertain.

• 정보보호학회논문지
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• 제33권4호
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• pp.621-631
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• 2023

적대적 학습은 적대적 샘플에 대한 딥러닝 모델의 강건성을 향상시킨다. 하지만 기존의 적대적 학습 기법은 입력단계의 작은 섭동마저도 은닉층의 특징에 큰 변화를 일으킨다는 점을 간과하여 adversarial loss function에만집중한다. 그 결과로 일반 샘플 또는 다른 공격 기법과 같이 학습되지 않은 다양한 상황에 대한 정확도가 감소한다. 이 문제를 해결하기 위해서는 특징 표현 능력을 향상시키는 모델 아키텍처에 대한 분석이 필요하다. 본 논문에서는 입력 이미지의 attention map을 생성하는 attention module을 일반 모델에 적용하고 PGD 적대적학습을수행한다. CIFAR-10 dataset에서의 제안된 기법은 네트워크 구조에 상관없이 적대적 학습을 수행한 일반 모델보다 적대적 샘플에 대해 더 높은 정확도를 보였다. 특히 우리의 접근법은 PGD, FGSM, BIM과 같은 다양한 공격과 더 강력한 adversary에 대해서도 더 강건했다. 나아가 우리는 attention map을 시각화함으로써 attention module이 적대적 샘플에 대해서도 정확한 클래스의 특징을 추출한다는 것을 확인했다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권11호
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• pp.4426-4442
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• 2020

Since scene segmentation is becoming a hot topic in the field of autonomous driving and medical image analysis, researchers are actively trying new methods to improve segmentation accuracy. At present, the main issues in image semantic segmentation are intra-class inconsistency and inter-class indistinction. From our analysis, the lack of global information as well as macroscopic discrimination on the object are the two main reasons. In this paper, we propose a Densely connected residual Attention network (DA-Res2Net) which consists of a dense residual network and channel attention guidance module to deal with these problems and improve the accuracy of image segmentation. Specifically, in order to make the extracted features equipped with stronger multi-scale characteristics, a densely connected residual network is proposed as a feature extractor. Furthermore, to improve the representativeness of each channel feature, we design a Channel-Attention-Guide module to make the model focusing on the high-level semantic features and low-level location features simultaneously. Experimental results show that the method achieves significant performance on various datasets. Compared to other state-of-the-art methods, the proposed method reaches the mean IOU accuracy of 83.2% on PASCAL VOC 2012 and 79.7% on Cityscapes dataset, respectively.

• ETRI Journal
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• 제44권3호
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• pp.438-449
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• 2022

This study proposes a dependency grammar-based self-attention multilayered bidirectional long short-term memory (DG-M-Bi-LSTM) model for subject-predicate-object (SPO) tuple recognition from natural language (NL) sentences. To add recent knowledge to the knowledge base autonomously, it is essential to extract knowledge from numerous NL data. Therefore, this study proposes a high-accuracy SPO tuple recognition model that requires a small amount of learning data to extract knowledge from NL sentences. The accuracy of SPO tuple recognition using DG-M-Bi-LSTM is compared with that using NL-based self-attention multilayered bidirectional LSTM, DG-based bidirectional encoder representations from transformers (BERT), and NL-based BERT to evaluate its effectiveness. The DG-M-Bi-LSTM model achieves the best results in terms of recognition accuracy for extracting SPO tuples from NL sentences even if it has fewer deep neural network (DNN) parameters than BERT. In particular, its accuracy is better than that of BERT when the learning data are limited. Additionally, its pretrained DNN parameters can be applied to other domains because it learns the structural relations in NL sentences.

• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.47-54
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• 2023

For Bengali music emotion classification, deep learning models, particularly CNN and RNN are frequently used. But previous researches had the flaws of low accuracy and overfitting problem. In this research, attention-based Conv1D and BiGRU model is designed for music emotion classification and comparative experimentation shows that the proposed model is classifying emotions more accurate. We have proposed a Conv1D and Bi-GRU with the attention-based model for emotion classification of our Bengali music dataset. The model integrates attention-based. Wav preprocessing makes use of MFCCs. To reduce the dimensionality of the feature space, contextual features were extracted from two Conv1D layers. In order to solve the overfitting problems, dropouts are utilized. Two bidirectional GRUs networks are used to update previous and future emotion representation of the output from the Conv1D layers. Two BiGRU layers are conntected to an attention mechanism to give various MFCC feature vectors more attention. Moreover, the attention mechanism has increased the accuracy of the proposed classification model. The vector is finally classified into four emotion classes: Angry, Happy, Relax, Sad; using a dense, fully connected layer with softmax activation. The proposed Conv1D+BiGRU+Attention model is efficient at classifying emotions in the Bengali music dataset than baseline methods. For our Bengali music dataset, the performance of our proposed model is 95%.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• 제27권1호
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• pp.48-55
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• 2016

Objectives: Children with attention-deficit hyperactivity disorder (ADHD) may have deficits in time perception, as assessed by the time estimation task and the time reproduction task, however its age-related trajectory is not yet determined. Therefore we examined the correlation between accuracy of time perception tasks and age, and the association between accuracy of estimation tasks and reproduction tasks. Methods: Sixty-three patients with ADHD, aged 8 to 18 years tested the tasks for five time durations (2, 4, 12, 45, and 60 seconds). Accuracy of tasks was assumed differences (absolute values) between raw results of tasks and original time durations. Spearman's correlation analysis was performed to determine correlation between accuracy of time perception tasks and age. Multivariate regression was used to determine the association of accuracy of estimation tasks with accuracy of reproduction tasks. Results: Age showed correlation with accuracy of estimation tasks, but not with that of reproduction tasks. We observed that the higher the accuracy in 12, 45, and 60 seconds duration time reproduction, the higher the accuracy in longer seconds duration time estimation. Conclusion: Age was correlated with time estimation accuracy whereas there was no impact on time reproduction accuracy. Association of each of the two time perception tasks, particularly in longer time duration, suggested specific impairments in time perception.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 춘계학술발표대회
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• pp.358-361
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• 2021

In order to improve the accuracy of small target detection more effectively, this paper proposes an improved single shot detector (SSD) target detection and recognition method based on cspdarknet53, which introduces lightweight ECA attention mechanism and Feature Pyramid Network (FPN). First, the original SSD backbone network is replaced with cspdarknet53 to enhance the learning ability of the network. Then, a lightweight ECA attention mechanism is added to the basic convolution block to optimize the network. Finally, FPN is used to gradually fuse the multi-scale feature maps used for detection in the SSD from the deep to the shallow layers of the network to improve the positioning accuracy and classification accuracy of the network. Experiments show that the proposed target detection algorithm has better detection accuracy, and it improves the detection accuracy especially for small targets.

• 한국음향학회지
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• 제39권5호
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• pp.406-413
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• 2020

본 논문에서는 조류와 양서류 울음소리의 구별 정확도를 높이기 위해 게이트 선형유닛과 자가주의 집중 모듈을 활용해서 데이터의 중요한 부분을 중심으로 특징 추출 및 데이터 프레임의 중요도를 판별해 구별 정확도를 높인다. 이를 위해 먼저 1차원의 음향 데이터를 로그 멜 스펙트럼으로 변환한다. 로그 멜 스펙트럼에서 배경잡음같이 중요하지 않은 정보는 게이트 선형유닛을 거쳐 제거한다. 그러고 난 뒤 시간 축에 자가주의집중기법을 적용해 구별 정확도를 높인다. 사용한 데이터는 자연환경에서 멸종위기종을 포함한 조류 6종의 울음소리와 양서류 8종의 울음소리로 구성했다. 그 결과, 게이트 선형유닛 알고리즘과 시간 축에서 자가주의집중을 적용한 구조의 평균 정확도는 조류를 구분했을 때 91 %, 양서류를 구분했을 때 93 %의 분류율을 보였다. 또한, 기존 알고리즘보다 약 6 % ~ 7 % 향상된 정확도를 보이는 것을 확인했다.

• 한국컴퓨터정보학회논문지
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• 제26권1호
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• pp.93-101
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• 2021

본 논문에서는 실제 도로의 다양한 상황에서도 차량 번호판을 정확하게 탐지하기 위해 차량 번호판의 후보 지역을 공간 집중 영역으로 사용하는 차량 번호판 탐지 모델을 제안하였다. 먼저, 기존의 WPOD-NET이 전처리 과정에서 검출된 차량 영역을 이용하기 때문에 넓은 탐지 후보 영역으로 인해 불필요한 노이즈가 포함되어 탐지 정확도가 낮아짐을 확인하였다. 이를 개선하기 위해 차량 번호판의 후보 지역을 공간 집중 영역으로 사용하는 차량 번호판 탐지 모델을 제안하였고, 제안한 방법이 기존 WPOD-NET보다 탐지 정확도를 어느 정도 개선하는지 분석하기 위해 GT 데이터를 기반으로 최적의 공간 집중 영역을 설정한 경우와 함께 탐지 정확도를 비교하였다. 실험에 따르면 제안된 모델이 기존 WPOD-NET에 비해 타이트한 탐지 후보 영역을 갖기 때문에 약 20% 더 높은 탐지 정확도를 보임을 확인하였다.