• The Transactions of the Korea Information Processing Society
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• v.13 no.10
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• pp.505-512
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• 2024

As the need for hygiene management increases due to COVID-19, the importance of non-contact services is gaining attention. Hands, a tool for expressing intentions and conveying information, are emerging as an alternative to computer input devices such as the keyboard and mouse. In this study, we propose a method to address public health problems that arise when using unmanned ordering machines by controlling a computer using hand gestures detected through a camera. The focus is on identifying frequently used hand gestures, especially the bending of the index finger. To this end, we develop a non-contact input device using the MediaPipe framework and the long short-term memory (LSTM) model. This approach can identify hand gestures in three-dimensional space and provides scenarios that can be applied to the fields of virtual reality (VR) and augmented reality (AR). It offers improved public health and user experience by presenting methods that can be applied to various situations such as navigation systems and unmanned ordering machines.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.348-369
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• 2024

With the advancement of Industry 4.0 and Industrial Internet of Things (IIoT), manufacturing increasingly seeks automation and intelligence. Temperature and vibration monitoring are essential for machinery health. Traditional abnormal state detection methodologies often overlook the intricate frequency characteristics inherent in vibration time series and are susceptible to erroneously reconstructing temperature abnormalities due to the highly similar waveforms. To address these limitations, we introduce synergistic, end-to-end, unsupervised Frequency-Time Domain Memory-Enhanced Autoencoders (FTD-MAE) capable of identifying abnormalities in both temperature and vibration datasets. This model is adept at accommodating time series with variable frequency complexities and mitigates the risk of overgeneralization. Initially, the frequency domain encoder processes the spectrogram generated through Short-Time Fourier Transform (STFT), while the time domain encoder interprets the raw time series. This results in two disparate sets of latent representations. Subsequently, these are subjected to a memory mechanism and a limiting function, which numerically constrain each memory term. These processed terms are then amalgamated to create two unified, novel representations that the decoder leverages to produce reconstructed samples. Furthermore, the model employs Spectral Entropy to dynamically assess the frequency complexity of the time series, which, in turn, calibrates the weightage attributed to the loss functions of the individual branches, thereby generating definitive abnormal scores. Through extensive experiments, FTD-MAE achieved an average ACC and F1 of 0.9826 and 0.9808 on the CMHS and CWRU datasets, respectively. Compared to the best representative model, the ACC increased by 0.2114 and the F1 by 0.1876.

• International Journal of Aeronautical and Space Sciences
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• v.16 no.2
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• pp.177-189
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• 2015

This paper presents an all-direct domain decomposition approach for large-scale structural analysis. The proposed approach achieves computational robustness and efficiency by enforcing the compatibility of the displacement field across the sub-domain boundaries via local Lagrange multipliers and augmented Lagrangian formulation (ALF). The proposed domain decomposition approach was compared to the existing FETI approach in terms of the computational time and memory usage. The parallel implementation of the proposed algorithm was described in detail. Finally, a preliminary validation was attempted for the proposed approach, and the numerical results of two- and three-dimensional problems were compared to those obtained through a dual-primal FETI approach. The results indicate an improvement in the performance as a result of the implementing the proposed approach.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.394-395
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• 2022

Exergames are playing an important role in healthcare/rehabilitation. It has also been used to improve motivation among patients with reduced cognition. The purpose of this pilot study was to evaluate the feasibility of using augmented reality (AR) with game-based cognitive-motor training programs for executive function, restricted and repetitive behaviors (RRBs) in children with autism spectrum disorder. Sixteen children aged 6 -16 years were randomly allocated to the experimental group and control group. Outcome measures were performed before and after the intervention and included executive function, restricted and repetitive behavior. A satisfactory survey was conducted post-intervention. A statistically significant improvement was observed in working memory and cognitive flexibility in the experimental group (P<0.05). However, despite no statistical improvements in cognitive inhibition and four subscales of RRBs, promising changes were observed in all the subscales of the executive function and the behavioral outcomes. Parents appreciated the program and children enjoyed the interaction with the AR game-based training. The findings of this preliminary feasibility study showed that AR using Kinect v2 motion with a cognitive-motor game content can be used for children with autism. However, there is a need for conducting a large-scale study to evaluate his effectiveness on executive function and restricted and repetitive behaviors.

• Journal of Industrial Convergence
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• v.19 no.1
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• pp.79-85
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• 2021

The purpose of this study was to investigate the existing augmented reality (AR) contents in education and to develop digital AR contents to promote the learning outcomes in nursing skills education. AR contents has been widely used in education such as children's books, coloring, or exhibition experiences, but rarely developed in nursing education. We wanted to develop AR contents on nursing skills which required memorization of complex contents. In order to improve nursing students' memorization skills, we developed AR techniques holding and executing cameras along with game elements of time and points. In order to give the effect of placing objects in front of the user's eyes, an augmented reality camera was applied, and a total of 40 levels were created to produce the sequence of nursing techniques. This study showed that learning contents in the medical field requiring expertise could be implemented as AR contents. The content developed in this study will be used as a learning aid for nursing students.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.538-541
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• 2014

With the increased market demand for advanced specifications in smart phones, smart phones that have functions with wireless communications with high speed, cameras with high pixel and with high graphic processing ability have appeared. Furthermore, as traditional Augmented Reality technology has been practicable in mobile devices, many application's use AR technology, so AR's portability has been increased. But application with AR technology, which was implemented in smart phones has created capacity issues as applications are taking up a large portion of the memory capacity of phones. This research designed and implemented optimized AR technology by Mixare, AR open source, to solve such problems. As a result, I assured that there has been a decrease in application's memory used on the basis of mobiles.

• Molecules and Cells
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• v.39 no.6
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• pp.468-476
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• 2016

PLZF-expressing invariant natural killer T cells and CD4 T cells are unique subsets of innate T cells. Both are selected via thymocyte-thymocyte interaction, and they contribute to the generation of activated/memory-like CD4 and CD8 T cells in the thymus via the production of IL-4. Here, we investigated whether $PLZF^+$ innate T cells also affect the development and function of $Foxp3^+$ regulatory CD4 T cells. Flow cytometry analysis of the thymus and spleen from both CIITA transgenic C57BL/6 and wild-type BALB/c mice, which have abundant $PLZF^+$ CD4 T cells and invariant natural killer T cells, respectively, revealed that $Foxp3^+$ T cells in these mice exhibited a $CD103^+$ activated/memorylike phenotype. The frequency of $CD103^+$ regulatory T cells was considerably decreased in $PLZF^+$ cell-deficient $CIITA^{Tg}Plzf^{lu/lu}$ and $BALB/c.CD1d^{-/-}$ mice as well as in an IL-4-deficient background, such as in $CIITA^{Tg}IL-4^{-/-}$ and $BALB/c.IL-4^{-/-}$ mice, indicating that the acquisition of an activated/ memory-like phenotype was dependent on $PLZF^+$ innate T cells and IL-4. Using fetal thymic organ culture, we further demonstrated that IL-4 in concert with TGF-${\beta}$ enhanced the acquisition of the activated/memory-like phenotype of regulatory T cells. In functional aspects, the activated/ memory-like phenotype of Treg cells was directly related to their suppressive function; regulatory T cells of $CIITA^{Tg}PIV^{-/-}$ mice more efficiently suppressed ovalbumin-induced allergic airway inflammation compared with their counterparts from wild-type mice. All of these findings suggest that $PLZF^+$ innate T cells also augmented the generation of activated/memory-like regulation via IL-4 production.

• Journal of information and communication convergence engineering
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• v.9 no.3
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• pp.266-270
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• 2011

The advancement of technology enables smart phones or handheld devices to render complex 3D graphics. However, the processing power and memory of smart phones remain very limited to render high polygon and details 3D models especially on games which requires animation, physic engine, or augmented reality. In this paper, several techniques will be introduced to speed up the computation and reducing the number of vertices of the 3D meshes without losing much detail.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.354-356
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• 1998

축약 분산 기억 장치는 적응적 문제 해결 능력과 하드웨어화의 용이성으로 인해 현실성이 있는 신경망의 한 모델로 주목받고 있다. 그러나 다층 인식자의 개별 뉴론이 선형의 결정 함수로 해 공간을 이분하고 그들이 다양하게 결합하므로써 일반적인 문제 해결 능력을 갖는데 비해, 축약 분산 기억 장치의 뉴론은 해 공간에서 자신을 중심으로 한 일정 반경 영역을 안과 밖으로 이분하고 이들을 단순하게 합하므로 해 공간이 크기 관계를 갖는 경우 비효율적인 모델로 된다. 본 논문에서는 이러한 축약 분산 기억 장치의 특성과 그 원인을 규명하고 해결 방안으로써 개선된 축약 분산 기억 장치를 제안한다. 아울러 새로운 모델의 적용 예를 ATM 호 수락 제어 과정을 통해 보인다.

• Journal of information and communication convergence engineering
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• v.21 no.2
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• pp.159-166
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• 2023

The COI gene is a sequence of approximately 650 bp at the 5' terminal of the mitochondrial Cytochrome c Oxidase subunit I (COI) gene. As an effective DeoxyriboNucleic Acid (DNA) barcode, it is widely used for the taxonomic identification and evolutionary analysis of species. We created a CNN-LSTM hybrid model by combining the gene features partially extracted by the Long Short-Term Memory ( LSTM ) network with the feature maps obtained by the CNN. Compared to K-Means Clustering, Support Vector Machines (SVM), and a single CNN classification model, after training 278 samples in a training set that included 15 genera from two orders, the CNN-LSTM hybrid model achieved 94% accuracy in the test set, which contained 118 samples. We augmented the training set samples and four genera into four orders, and the classification accuracy of the test set reached 100%. This study also proposes calculating the cosine similarity between the training and test sets to initially assess the reliability of the predicted results and discover new species.