• Smart Media Journal
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• v.12 no.11
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• pp.1-9
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• 2023

Stress is a significant issue in modern society, often triggered by external or internal factors that are difficult to manage. When high stress persists over a long term, it can develop into a chronic condition, negatively impacting health and overall well-being. However, it is challenging for individuals experiencing chronic stress to recognize their condition, making early detection and management crucial. Using biosignals measured from wearable devices to detect stress could lead to more effective management. However, there are two main problems with using biosignals: first, manually extracting features from these signals can introduce bias, and second, the performance of classification models can vary greatly depending on the subject of the experiment. This paper proposes a model that reduces bias using convo utional autoencoders, which can represent the key features of data, and enhances generalizability by employing soft voting, a method of ensemble learning, to minimize performance variability. To verify the generalization performance of the model, we evaluate it using LOSO cross-validation method. The model proposed in this paper has demonstrated superior accuracy compared to previous studies using the WESAD dataset.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.723-730
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• 2024

In order to trace the changes in the concept and understanding of automatically generated images, this study analogously explores the creative methods of photography and cinema, which represent the existing image fields, in terms of AI-based image creation methods and 'automaticity', and discusses the understanding and possibilities of new automatic image creation. At the time of the invention of photography and cinema, the field of 'automatic creation' was established for them in comparison to traditional art genres such as painting. Recently, as AI has been applied to video production, the concept of 'automatic creation' has been expanded, and experimental creations that freely cross the boundaries of literature, art, photography, and film are active. By utilizing technologies such as machine learning and deep learning, AI automated creation allows AI to perform the creative process independently. Automated creation using AI can greatly improve efficiency, but it also risks compromising the personal and subjective nature of art. The problem stems from the fact that AI cannot completely replace human creativity.

• Journal of Information Technology Applications and Management
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• v.30 no.1
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• pp.21-32
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• 2023

This study presents a way to design picture books that allow children to experience indirectly through realistic media at home and educational institutions for children whose direct experiences and activities are reduced offline in the post-COVID-19 era. The development and popularization of realistic media such as AR continues to be grafted and developed in the picture book field, and the advantage of this picture book is that children at the stage before learning letters can feel the story and character situation of the book through the experience of AR and QR animation in the picture book. In this process, you can get access to imaginary areas that can not be experienced in flat-panel illustrations with spatial animation to help improve the scalability and literacy of the story contained on the page. Children's education through realistic animation can increase the learning effect and needs to continue after COVID-19. Therefore, by presenting the process of viewing and experiencing picture books using AR and QR, the result of the study suggests that the design process of realistic picture books is not just a combination of technology, but a process of expanding children's perception and imagination. This study aims to find out how visual perception is designed differently by analyzing the differences and characteristics of each animation design, focusing on the AR and QR picture book "Where am I?" published by the researcher, and to contribute to the active introduction and development of realistic animation in the field of children's picture books and education.

• Smart Media Journal
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• v.13 no.7
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• pp.27-35
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• 2024

Since COVID-19, the education industry has shifted many of its activities to non-face-to-face channels, and many programs are still being conducted in non-face-to-face channels during the endemic. This study aims to analyze the quality factors affecting user satisfaction of synchronous and asynchronous channels operated non-face-to-face and compare the differences between the two channels. For this purpose, we analyzed the reviews of graduate students of the same content in the synchronous and asynchronous channels at H University in Seoul, Korea. The non-face-to-face learning quality factors derived through topic modeling are educational system quality, content quality, instructor quality, and expectation confirmation, and their effects on user satisfaction were empirically analyzed through survey responses related to these quality factors. The results showed that all of the quality factors had a significant positive effect on satisfaction, and there were significant differences in satisfaction factors between the two channels. This study is significant in that it suggests that improving the content itself for synchronous channels and improving the convenience of the program operation system for asynchronous channels is effective in improving user satisfaction.

• Smart Media Journal
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• v.13 no.7
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• pp.36-44
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• 2024

Identifying plant species and diseases is crucial for maintaining biodiversity and achieving optimal crop yields, making it a topic of significant practical importance. Recent studies have extended plant disease recognition from traditional closed-set scenarios to open-set environments, where the goal is to reject samples that do not belong to known categories. However, in open-world tasks, it is essential not only to define unknown samples as "unknown" but also to classify them further. This task assumes that images and labels of known categories are available and that samples of unknown categories can be accessed. The model classifies unknown samples by learning the prior knowledge of known categories. To the best of our knowledge, there is no existing research on this topic in plant-related recognition tasks. To address this gap, this paper utilizes knowledge distillation to model the category space relationships between known and unknown categories. Specifically, we identify similarities between different species or diseases. By leveraging a fine-tuned model on known categories, we generate pseudo-labels for unknown categories. Additionally, we enhance the baseline method's performance by using a larger pre-trained model, dino-v2. We evaluate the effectiveness of our method on the large plant specimen dataset Herbarium 19 and the disease dataset Plant Village. Notably, our method outperforms the baseline by 1% to 20% in terms of accuracy for novel category classification. We believe this study will contribute to the community.

• Smart Media Journal
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• v.13 no.7
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• pp.9-17
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• 2024

Due to the increase in energy consumption, and eco-friendly policies, there is a need for efficient energy consumption in buildings. Anomaly power detection based on deep learning are being used. Because of the difficulty in collecting anomaly data, anomaly detection is performed using reconstruction error with a Recurrent Neural Network(RNN) based autoencoder. However, there are some limitations such as the long time required to fully learn temporal features and its sensitivity to noise in the train data. To overcome these limitations, this paper proposes the TCN-USAD, combined with Temporal Convolution Network(TCN) and UnSupervised Anomaly Detection for multivariate data(USAD). The proposed model using TCN-based autoencoder and the USAD structure, which uses two decoders and adversarial training, to quickly learn temporal features and enable robust anomaly detection. To validate the performance of TCN-USAD, comparative experiments were performed using two building energy datasets. The results showed that the TCN-based autoencoder can perform faster and better reconstruction than RNN-based autoencoder. Furthermore, TCN-USAD achieved 20% improved F1-Score over other anomaly detection models, demonstrating excellent anomaly detection performance.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.547-550
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• 2009

The object recognition mechanism of human being is not well understood yet. On research of animal experiment using an ape, however, neurons that respond to simple shape (e.g. circle, triangle, square and so on) were found. And Hypothesis has been set up as human being may recognize object as combination of such simple shapes. That mechanism is called Figure Alphabet Hypothesis, and those simple shapes are called Figure Alphabet. As one way to research object recognition algorithm, we focused attention to this Figure Alphabet Hypothesis. Getting idea from it, we proposed the feature extraction algorithm for object recognition. In this paper, we described recognition of binarized images of multifont alphabet characters by the recognition model which combined three-layered neural network in the feature extraction algorithm. First of all, we calculated the difference between the learning image data set and the template by the feature extraction algorithm. The computed finite difference is a feature quantity of the feature extraction algorithm. We had it input the feature quantity to the neural network model and learn by backpropagation (BP method). We had the recognition model recognize the unknown image data set and found the correct answer rate. To estimate the performance of the contriving recognition model, we had the unknown image data set recognized by a conventional neural network. As a result, the contriving recognition model showed a higher correct answer rate than a conventional neural network model. Therefore the validity of the contriving recognition model could be proved. We'll plan the research a recognition of natural image by the contriving recognition model in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.2938-2956
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• 2020

Objective image quality assessment (IQA) models have been developed by effective features to imitate the characteristics of human visual system (HVS). Actually, HVS is extremely sensitive to color degradation and complex texture changes. In this paper, we firstly reveal that many existing full reference image quality assessment (FR-IQA) methods can hardly measure the image quality with contrast and masking texture changes. To solve this problem, considering texture masking effect, we proposed a novel FR-IQA method, called Texture and Color Quality Index (TCQI). The proposed method considers both in the masking effect texture and color visual perceptual threshold, which adopts three kinds of features to reflect masking texture, color difference and structural information. Furthermore, random forest (RF) is used to address the drawbacks of existing pooling technologies. Compared with other traditional learning-based tools (support vector regression and neural network), RF can achieve the better prediction performance. Experiments conducted on five large-scale databases demonstrate that our approach is highly consistent with subjective perception, outperforms twelve the state-of-the-art IQA models in terms of prediction accuracy and keeps a moderate computational complexity. The cross database validation also validates our approach achieves the ability to maintain high robustness.

• Journal of Broadcast Engineering
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• v.26 no.5
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• pp.489-499
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• 2021

This paper proposes an arts and culture content creation tool powered by artificial intelligence. With the recent advances in technologies including artificial intelligence, there are active research activities on creating art and culture contents. However, it is still difficult and cumbersome for those who are not familiar with programming and artificial intelligence. In order to deal with the content creation with new technologies, we analyze related creation tools, services and technologies that process with raw visual and audio data, generate new media contents and visualize intermediate results. We then extract key requirements for a future creation tool for creators who are not familiar with programming and artificial intelligence. We finally introduce an intuitive and integrated content creation tool for end-users. We hope that this tool will allow creators to intuitively and creatively generate new media arts and culture contents based on not only understanding given data but also adopting new technologies.

• The korean journal of orthodontics
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• v.53 no.5
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• pp.328-335
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• 2023

Objective: The aim of this study was to determine whether an association between study quality, other study characteristics, and Altmetric Attention Scores (AASs) existed in orthodontic studies. Methods: The Scopus database was searched to identify orthodontic studies published between January 1, 2017, and December 31, 2019. Articles that satisfied the eligibility criteria were included in this study. Study characteristics, including study quality were extracted and entered into a pre-pilot data collection sheet. Descriptive statistics were calculated. On an exploratory basis, random forest and gradient boosting machine learning algorithms were used to examine the influence of article characteristics on AAS. Results: In total, 586 studies with an AAS were analyzed. Overall, the mean AAS of the samples was 5. Twitter was the most popular social media platform for publicizing studies, accounting for 53.7%. In terms of study quality, only 19.1% of the studies were rated as having a high level of quality, with 41.8% of the studies deemed moderate quality. The type of social media platform, number of citations, impact factor, and study type were among the most influential characteristics of AAS in both models. In contrast, study quality was one of the least influential characteristics on the AAS. Conclusions: Social media platforms contributed the most to the AAS for orthodontic studies, whereas study quality had little impact on the AAS.