• 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.

• Journal of The Korean Society of Integrative Medicine
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• v.2 no.2
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• pp.13-20
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• 2014

Purpose : The purpose of this study was to evaluate the influence of cognition and executive function by computer - based attention program in vascular dementia. Method : The subjects of this study, old man diagnosed with vascular dementia, 12 patients were picked up, who were agreed with this research and were having hospital care for 4 weeks at nursing care centers. Computer-based attention program was applied to vascular dementia. Cognitive function measured by a K-MMSE and executive function measured by ACL. The SPSS Ver. 18.0 statistical program was used for data processing. The significance level for statistical inspection was set as 0.05. Result : In comparison of cognitive function was not significantly correlated in the pre and post test and executive function was significantly correlated in the pre and post test. Conclusion : Therefore, the computer-based attention program is useful to improve the cognitive and executive function in elderly with vascular dementia.

• Journal of Digital Policy
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• v.2 no.1
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• pp.9-19
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• 2023

ESG is a kind of financial data that pays more attention to corporate environment, social responsibility and corporate governance. This study explores the relationship between ESG and corporate sustainable development through empirical analysis. This study uses the regression method of fixed effects to conduct empirical research on the data of China's A-share listed companies from 2015 to 2020. The research results show that: good ESG performance can promote the sustainable development of enterprises. At the same time, the higher the attention, the better the ESG performance can promote the sustainable development of enterprises. This study enriches the related research on ESG and has certain reference value for promoting the sustainable development of enterprises.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.41 no.5
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• pp.518-524
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• 2022

In this paper, we propose a method for combining convolutional neural networks and attention mechanism to improve the recognition performance of emotional stress from Electroencephalogram (EGG) signals. In the proposed method, EEG signals are decomposed into five frequency domains, and spatial information of EEG features is obtained by applying a convolutional neural network layer to each frequency domain. As a next step, salient frequency information is learned in each frequency band using a gate transformer-based attention mechanism, and complementary frequency information is further learned through inter-frequency mapping to reflect it in the final attention representation. Through an EEG stress recognition experiment involving a DEAP dataset and six subjects, we show that the proposed method is effective in improving EEG-based stress recognition performance compared to the existing methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.5 s.305
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• pp.123-136
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• 2005

We propose a recognition system for superimposed patterns based on selective attention model and SVM which produces better performance than artificial neural network. The proposed selective attention model includes attention layer prior to SVM which affects SVM's input parameters. It also behaves as selective filter. The philosophy behind selective attention model is to find the stopping criteria to stop training and also defines the confidence measure of the selective attention's outcome. Support vector represents the other surrounding sample vectors. The support vector closest to the initial input vector in consideration is chosen. Minimal euclidean distance between the modified input vector based on selective attention and the chosen support vector defines the stopping criteria. It is difficult to define the confidence measure of selective attention if we apply common selective attention model, A new way of doffing the confidence measure can be set under the constraint that each modified input pixel does not cross over the boundary of original input pixel, thus the range of applicable information get increased. This method uses the following information; the Euclidean distance between an input pattern and modified pattern, the output of SVM, the support vector output of hidden neuron that is the closest to the initial input pattern. For the recognition experiment, 45 different combinations of USPS digit data are used. Better recognition performance is seen when selective attention is applied along with SVM than SVM only. Also, the proposed selective attention shows better performance than common selective attention.

• Journal of the Korea Society of Computer and Information
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• v.13 no.2
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• pp.87-94
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• 2008

In this paper, an extraction method of objects of interest in the color images is proposed. It is possible to extract objects of interest from a complex background without any prior-knowledge based on the proposed method. For object extraction, Gator images that contain information of object location, are created by using Gator filter. Based on the images the initial location of attention windows is determined, from which image features are selected to extract objects. To extract object, I modify the previous method partially and apply the modified method. To evaluate the performance of propsed method, precision, recall and F-measure are calculated between the extraction results from propsed method and manually extracted results. I verify the performance of the proposed methods based on these accuracies. Also through comparison of the results with the existing method, I verily the superiority of the proposed method over the existing method.

• Journal of Digital Convergence
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• v.13 no.8
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• pp.525-533
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• 2015

Purpose: The purpose of this study was designed to find out the effect of game and mandala convergence approach on the attention ability of the with school aged children. Method : The subjects of study were 30 children(11 male, 19 female) with school aged children who were attending in A, B community children center located in B city. The intervention was administered for 4 weeks : 3 times a weeks and 12 sessions in total. They were separated into a mandala group(n=10), game group(n=10) and control group(n=10). The experimental group was provided game and mandala program for 4 weeks. Each group examined though FAIR attention and concentration test. Result : The results of this study showed that the game and mandala program was effective to improved attention ability of the school aged children. Conclusion: This study concludes that game and mandala program had effect on the improvement of children's attention.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.687-689
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• 2018

The current EEG device can determine the concentration, but can not determine the concentration of the state. Therefore, we distinguish attitude based on Mindwave Attention data and additionally Pitch data to distinguish whether or not we are looking at a video object, and suggest a method to obtain better performance. Attention data were measured in the state where the images were viewed and concentrated. In the case of the Pitch data, Sit was measured when sitting on a desk and Lie when lying down. Attention value was 38 or more. When the value of the Pitch is smaller than -48, it is judged that it is in a prone state. When the concentration and sitting state were satisfied with this threshold value, it was judged that they focused on watching the actual video.

• Anxiety and mood
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• v.5 no.2
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• pp.96-102
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• 2009

Objective : We investigated that ADHD categorical diagnosis and the dimensional tools for the evaluation of ADHD, widely used in the clinical field, such as the child behavior check list- Korean version (K-CBCL), Korean personality inventory for children (KPI-C), computerized Attention Diagnostic System (ADS). Method : The DSM-IV clinical diagnosis applied by child psychiatrist. K-CBCL, KPI-C, ADS are used. Ultimately, totally 161 ADHD children and 161 controls were evaluated. Subject group are consist of 202 boys (62.7%) and 120 girls (37.3%), and the mean age was $9.5{\pm}2.0$ years old. Results and Conclusion : Social problem, and attention problem in the K-CBCL, correct response time standard deviation in the computerized ADS were statistically significant different and attention problem in the K-CBCL, hyperactivity subscale in the KPI-C were significant trait, between subject group and control group. The ROC value of attention problem in the K-CBCL, hyperactivity subscale in the KPI-C, and ADS were .78, .93, .86. Finally, we found that K-CBCL, KPI-C, ADS were significant corelation with the ADHD categorical diagnosis.