• Biomolecules & Therapeutics
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• 제26권4호
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• pp.368-373
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• 2018

Rapid eye movement (REM) sleep has an essential role in the process of learning and memory in the hippocampus. It has been reported that linalool, a major component of Lavandula angustifolia, has antioxidant, anti-inflammatory, and neuroprotective effects, along with other effects. However, the effect of linalool on the cognitive impairment and behavioral alterations that are induced by REM-sleep deprivation has not yet been elucidated. Several studies have reported that REM-sleep deprivation-induced memory deficits provide a well-known model of behavioral alterations. In the present study, we examined whether linalool elicited an anti-stress effect, reversing the behavioral alterations observed following REM-sleep deprivation in mice. Furthermore, we investigated the underlying mechanism of the effect of linalool. Spatial memory and learning memory were assessed through Y maze and passive avoidance tests, respectively, and the forced swimming test was used to evaluate anti-stress activity. The mechanisms through which linalool improves memory loss and behavioral alterations in sleep-deprived mice appeared to be through an increase in the serotonin levels. Linalool significantly ameliorated the spatial and learning memory deficits, and stress activity observed in sleep-deprived animals. Moreover, linalool led to serotonin release, and cortisol level reduction. Our findings suggest that linalool has beneficial effects on the memory loss and behavioral alterations induced by REM-sleep deprivation through the regulation of serotonin levels.

• Current Optics and Photonics
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• 제2권2호
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• pp.153-164
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• 2018

In semiconductor manufacturing, critical dimensions indicate the features of patterns formed by the semiconductor process. The purpose of measuring critical dimensions is to confirm whether patterns are made as intended. The deposition process for an organic light emitting diode (OLED) forms a luminous organic layer on the thin-film transistor electrode. The position of this organic layer greatly affects the luminescent performance of an OLED. Thus, a system for measuring the position of the organic layer from outside of the vacuum chamber in real-time is desired for monitoring the deposition process. Typically, imaging from large stand-off distances results in low spatial resolution because of diffraction blur, and it is difficult to attain an adequate industrial-level measurement. The proposed method offers a new superresolution single-image using a conversion formula between two different optical systems obtained by a deep learning technique. This formula converts an image measured at long distance and with low-resolution optics into one image as if it were measured with high-resolution optics. The performance of this method is evaluated with various samples in terms of spatial resolution and measurement performance.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2019년도 춘계학술대회
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• pp.439-441
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• 2019

딥러닝은 객체인식 분야에서에서 강력하고, 강건한 학습 알고리즘이다. 딥러닝에서 자주 활용되고, 객체인식 분야에서 최고의 성능을 보여주는 네트워크는 Convolutional Neural Network(CNN) 이다. 숫자 필기 인식을 위한 MNIST 데이터셋를 CNN으로 학습하면 성능이 매우 뛰어나다. 이는 MNIST 데이터 셋의 숫자들이 중앙에 잘 정렬되어 있기 때문이다. 하지만, 실제 데이터들은 중앙에 정렬이 잘 되어있지 않다. 이러한 경우에 CNN은 이전과 같이 우수한 성능을 보여주지 못한다. 이를 해결하기 위해, 우리는 FFT를 활용하여 이미지를 주파수 공간으로 변환하여 입력으로 주는 방법을 제안한다.

• 전기전자학회논문지
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• 제24권4호
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• pp.1148-1155
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• 2020

Fire must be extinguished as quickly as possible because they cause a lot of economic loss and take away precious human lives. Especially, the detection of smoke, which tends to be found first in fire, is of great importance. Smoke detection based on image has many difficulties in algorithm research due to the irregular shape of smoke. In this study, we introduce a new real-time smoke detection algorithm that reduces the detection of false positives generated by irregular smoke shape based on faster r-cnn of factory-installed surveillance cameras. First, we compute the global frame similarity and mean squared error (MSE) to detect the movement of smoke from the input surveillance camera. Second, we use deep learning algorithm (Faster r-cnn) to extract deferred candidate regions. Third, the extracted candidate areas for acting are finally determined using space and temporal features as smoke area. In this study, we proposed a new algorithm using the space and temporal features of global and local frames, which are well-proposed object information, to reduce false positives based on deep learning techniques. The experimental results confirmed that the proposed algorithm has excellent performance by reducing false positives of about 99.0% while maintaining smoke detection performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권6호
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• pp.2031-2048
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• 2021

Not only can air pollution reduce the overall competitiveness of tourist destinations, but also changes tourists' travel decisions, thereby affecting the tourism flows. The study presents a machine learning method to analyze how the haze pollution puts spatial effect on tourism flows in China from 2001 to 2018, and reveals the regional differences in heterogeneity among eastern, central, and western China. Our investigation reveals three interesting observations. First, the Environmental Kuznets Curve of the impact of haze pollution on tourism flows is not significant. In the eastern and western regions, the interaction between haze pollution and domestic tourism flows as well as inbound tourism flows shows an inverted U-shaped curve respectively. Second, there is an significantly positive spillover effect of tourism flows in all of the eastern, central, and western regions. As to the intensity of spillover, domestic tourism flows is higher than that of the inbound tourism flows. Both of the above figures are greatest in the eastern. Third, the Chinese haze pollution mainly reduces the inbound tourism flows, and only imposes significantly negative direct effects on the domestic tourism flows in the central region. In the central and eastern regions, significantly negative direct effects and spillover effects are exerted on inbound tourism.

• 한국공간구조학회논문집
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• 제23권3호
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• pp.35-43
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• 2023

This study aims to evaluate the structural safety of a structural thermal barrier, installed inside the structure of a building and performed the role of a load-bearing element and an insulation simultaneously, contributing to the realization of net-zero buildings. To ensure the reliability of the analysis model, the analysis results derived from LS-DYNA were compared with the experimental results. Based on the results shown through the flexural experiment, the reliability of the thermal cross-section insulation structure model for slabs was validated. In addition, the effect of the UHPC block on the load support performance and its contribution to vertical deflection was verified.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.148-148
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• 2022

Spatiotemporal precipitation data is one of the primary quantities in hydrological as well as climatological studies. Despite the fact that the estimation of these data has made considerable progress owing to advances in remote sensing, the discrepancy between satellite-derived precipitation product (SPP) data and observed data is still remarkable. This study aims to propose an effective deep learning model (DLM) for bias correction of SPPs. In which TRMM (The Tropical Rainfall Measuring Mission), CMORPH (CPC Morphing technique), and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) are three SPPs with a spatial resolution of 0.25o exploited for bias correction, and APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) data is used as a benchmark to evaluate the effectiveness of DLM. We selected the Mekong River Basin as a case study area because it is one of the largest watersheds in the world and spans many countries. The adjusted dataset has demonstrated an impressive performance of DLM in bias correction of SPPs in terms of both spatial and temporal evaluation. The findings of this study indicate that DLM can generate reliable estimates for the gridded satellite-based precipitation bias correction.

• Educational Technology International
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• 제9권1호
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• pp.79-96
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• 2008

The issue of connection between entities has a lengthy history in educational research, especially since it provides the necessary bridge between base and target in analogical transfer. Recently, the connection has been viewed through the application of technology to bridge between sequences in order to be cognitively useful. This study reports the effect of sequence type (AT vs. TA) and connection type (fading vs. popping) on the achievement and analogical transfer in a multimedia application. In the current research, 10th -grade and 11th -grade biology students in Korea were randomly assigned to five groups to test the effects of presentation sequence and entity connection type on analogical transfer. Consistent with previous studies, sequence type has a significant effect: analogical transfer performance was better when base representations were presented first followed by target representations rather than the reverse order. This is probably because presenting a familiar base first helps in understanding a less familiar target. However, no fully significant differences were found with the entity connection types (fading vs. popping) in analogical transfer. According to the Markman and Gentner's (2005) spatial model, analogy in a space is influenced only by the differences between concepts, not by distance in space. Thus connection types fail on the basis of this spatial model in analogical transfer test. The findings and their implications for sequence and connection research and practice are discussed. Leveraging on the analogical learning process, specific implications for scaffolding learning processes and the development of adaptive expertise are drawn.

• 한국공간구조학회논문집
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• 제24권2호
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• pp.53-63
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• 2024

The cultural heritage of fortresses is often exposed to external elements, leading to significant damage from stone weathering and natural disasters. However, due to the nature of cultural heritage, dismantling and restoration are often impractical. Therefore, the stability of fortress cultural heritage was evaluated through non-destructive testing. The durability of masonry cultural heritages is greatly influenced by the physical characteristics of the back-fille material. Dynamic characteristics were assessed, and endoscopy was used to inspect internal fillings. Additionally, a finite element analysis model was developed considering the surrounding ground through elastic wave exploration. The analysis showed that the loss of internal fillings in the target cultural heritage site could lead to further deformation in the future, emphasizing the need for careful observation.

• Journal of Platform Technology
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• 제11권6호
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• pp.21-27
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• 2023

In this paper, we introduce a reinforcement learning-based algorithm for personalized tourist path recommendations. The algorithm employs a reinforcement learning agent to explore tourist regions and identify optimal paths that are expected to enhance tourism experiences. The concept of tourism experience is defined through points of interest (POI) located along tourist paths within the tourist area. These metrics are quantified through aggregated evaluation scores derived from reviews submitted by past visitors. In the experimental setup, the foundational learning model used to find tour paths is the Deep Q-Network (DQN). Despite the limited availability of historical tourist behavior data, the agent adeptly learns travel paths by incorporating preference scores of tourist POIs and spatial information of the travel area.