• Journal of Engineering Education Research
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• v.26 no.2
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• pp.32-44
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• 2023

This research aims to examine the role of writing self-efficacy in engineering students' writing metacognitive strategies and writing apprehension. To achieve this purpose, first, the relationships among writing self-efficacy, writing metacognitive strategies and writing apprehension were investigated. Second, the effects of writing self-efficacy, as perceived by engineering students, on writing metacognitive strategies as well as writing apprehension were explored. A total of 173 engineering students from one university in Korea responded to survey based on a three-variables scale. The findings were that, firstly, positive correlations between writing self-efficacy and writing metacognitive strategies were identified in terms of sub-factors of those two variables. Secondly, negative correlations between writing apprehension and writing self-efficacy, and between writing apprehension and writing metacognitive strategies, were identified in terms of sub-factors of those variables. Thirdly, writing self-efficacy predicted engineering students' writing metacognitive strategies' sub-factors and writing apprehension. The practical implications of these findings are discussed herein, with particular attention on education for promotion of writing self-efficacy and reduction of writing apprehension.

• Journal of East-West Nursing Research
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• v.21 no.1
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• pp.10-17
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• 2015

Purpose: The purpose of this study was to investigate nursing professionalism and affecting factors of nursing professionalism in freshman nursing students. Methods: Pre and post-test study design was used to compare the differences of sutdy variables at the completion of a fundamental nursing course. A convenience sample of 164 were recruited. The date were collected from September 1st to December 1st in 2012. Results: The mean scores of the nursing professionalism were significantly increased from a pre-test (3.73) to post-test (3.93). The mean scores of the self-esteem were significantly increased from a pre-test (3.05) to post-test (3.13). Self-esteem and satisfaction with nursing were factors that affect nursing professionalism. Conclusion: Attention should be given to nursing education to cultivate nursing professionalism by improving self-esteem and satisfaction with nursing in nursing students.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.61-73
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• 2023

As smartphone use is increasing within the middle-aged population, society should pay closer attention to the mental health problems associated with smartphone addiction. This study examines the possibility that depression, anxiety, and ADHD can be interpreted not only as negative aspects, but also as positive aspects, in an addiction-related individual. We used habitual and addictive smartphone behavior as the dependent variables; anxiety, ADHD, depression, and habitual smartphone use as the independent variables; and self-control as a moderating variable. Depression and ADHD in smartphone users were found to be associated with higher levels of addictive smartphone use. Anxiety was having negative effect on addictive smartphone use. However, habitual smartphone use didn't significantly affect addictive smartphone use. Further analysis indicated that depression, anxiety, and ADHD have mediating effects on habitual smartphone use. This study confirmed that psychological factors in adults, as well as habitual/addictive smartphone use and self-control, significantly influence smartphone overdependence.

• Korean Institute of Interior Design Journal
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• v.25 no.2
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• pp.41-49
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• 2016

The purpose of this study is to analyze the change and to compare to the difference of 'selective elements' and 'space-memory' focusing on the theory, 'selective attention' through the survey results. In this study, In this study, the lighting is considered a important factor in the change of 'selective elements'. this survey is to find the selective elements of participants and to measure the spatial sensitivity of respondents through 'self- test'. The analysis in this study is conducted by descriptive statistics, t-test and one way ANOVA by SPSS program 22. The results of this study are as following; Firstly, 'attention-element' could be classified with 4 types, 'shape', 'material', 'contrast' and 'combination'. 'shape' could divide into 'structure' and' furniture and object'. In case of 'material', it could section with 'pattern' and 'color'. Secondly, through the results of study, 'attention-element' is different each space during the day in detail. But we could know that 'shape' is the important element of the 'attention-elements' during the day through comparison of this result. That means users consider this as a important factor when they evaluate the space. Therefore, it is effective way designers to consider 'shape' as the first element when they want to conduct the special sensitivity of users in the space through planning. On the other hand, what selective elements of users are different by the lighting situation should be acknowledged by designers. And they should think the kinds of selective elements are more various when lighting turns on than turns off.. Thirdly, through the results such as the meaningful difference of space-memory of users according to the change of 'attention-elements', designers should judge about which kind of feeling of users to the space do you want lead in the design process. For the effective feedback between spaces and users to induce the same emotion of users, designers need to consider the unified design and the individual design both. Also, we will regard the differences in the users' emotion to the space according to the lighting situation when we design the space.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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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.

• The Journal of the Acoustical Society of Korea
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• v.41 no.3
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• pp.342-350
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• 2022

In this paper, actual drone noise samples are collected for speech processing in disaster environments to build noise-corrupted speech database, and speech enhancement performance is evaluated by applying spectrum subtraction and mask-based speech enhancement techniques. To improve the performance of VoiceFilter (VF), an existing deep neural network-based speech enhancement model, we apply the Self-Attention operation and use the estimated noise information as input to the Attention model. Compared to existing VF model techniques, the experimental results show 3.77%, 1.66% and 0.32% improvements for Source to Distortion Ratio (SDR), Perceptual Evaluation of Speech Quality (PESQ), and Short-Time Objective Intelligence (STOI), respectively. When trained with a 75% mix of speech data with drone sounds collected from the Internet, the relative performance drop rates for SDR, PESQ, and STOI are 3.18%, 2.79% and 0.96%, respectively, compared to using only actual drone noise. This confirms that data similar to real data can be collected and effectively used for model training for speech enhancement in environments where real data is difficult to obtain.

• Smart Media Journal
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• v.10 no.1
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• pp.63-69
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• 2021

When a person sees a sentence and understands the sentence, the person understands the sentence by reminiscent of the main word in the sentence as an image. Text-to-image is what allows computers to do this associative process. The previous deep learning-based text-to-image model extracts text features using Convolutional Neural Network (CNN)-Long Short Term Memory (LSTM) and bi-directional LSTM, and generates an image by inputting it to the GAN. The previous text-to-image model uses basic embedding in text feature extraction, and it takes a long time to train because images are generated using several modules. Therefore, in this research, we propose a method of extracting features by using the attention mechanism, which has improved performance in the natural language processing field, for sentence embedding, and generating an image by inputting the extracted features into the GAN. As a result of the experiment, the inception score was higher than that of the model used in the previous study, and when judged with the naked eye, an image that expresses the features well in the input sentence was created. In addition, even when a long sentence is input, an image that expresses the sentence well was created.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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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.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.19 no.2
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• pp.120-127
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• 2008

Objectives : The aim of this study was to examine the effects of maternal sociodemographic characteristics, especially education and employment, and parenting stress on the child's self-concept. Attention was also paid to the mediating impact of parenting style on the relationship between the maternal variables and a child's self-concept. Methods : A questionnaire and three scales were completed by fifth graders and their mothers, and 270 sets of paired data were analyzed. Results : The results showed that mothers with higher education experienced less child-related parenting stress, were perceived to be more achievement-oriented and rational by their children, and also had children with more positive self-concept. The relationship between maternal education and child's self-concept was mediated by the rationality dimension of parenting style. Maternal employment was not related to parenting stress, parenting style and most aspects of the child's self-concept. Lastly, child-related parenting stress had a negative effect on the child's self-concept, and this effect was mediated by the warmth and rationality dimensions of parenting style. Conclusion : Parenting style had a mediating effect on the relationships between the child's self-concept and maternal education and child-related parenting stress.

• Proceedings of the Korean Vacuum Society Conference
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• 2011.02a
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• pp.24-25
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• 2011

Molecular self-assembly has several advantages over other nanofabrication methods. Molecular building blocks ensure ultrafine pattern precision, parallel structure formation allows for mass production and a variety of three-dimensional structures are available for fabricating complex structures. Nevertheless, the molecular interaction for self-assembly generally relies on weak forces such as van der Waals force, hydrogen bonding, or hydrophobic interaction. Due to the weak interaction, the structure formation is usually slow and the degree of ordering is low in a self-assembled structure. To promote self-assembly, directed assembly methods employing prepatterned substrates or external fields have been developed and gathered a great deal of technological attention as a next generation nanofabrication process. In this presentation a variety of directed assembly methods for soft nanomaterials including block copolymers, peptides and carbon nanomaterials will be introduced. Block copolymers are representative self-assembling materials extensively utilized in nanofabrication. In contrast to colloid assembly or anodized metal oxides, various shapes of nanostructures, including lines or interconnected networks, can be generated with a precise tunability over their shape and size. Applying prepatterned substrates$^{1,2}$ or introducing thickness modulation$^3$ to block copolymer thin films allowed for the control over the orientational and positional orderings of self-assembled structures. The nanofabrication processes for metals, semiconductors$^4$, carbon nanotubes$^{5,6}$, and graphene$^{6,7}$ templating block copolymer self-assembly will be presented.