• Korean Journal of Heritage: History & Science
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• v.33
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• pp.182-208
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• 2000

Kwan-gyongdo is a pictorial presentation of a $s{\bar{u}}tra$ that teaches the Buddhist way for a person to be reincarnated in the paradise of $Amit{\bar{a}}bha$ Buddha. It consists of a preface (Kwan-gyong sobun pyonsangdo) and 16 scenes of $S{\bar{a}}kyamuni$ preaching. The preface, a painting illustrating the motivation behind the production of tile kwan-gyongdo, illustrates the "Legend of King $Aj{\bar{a}}tasatru$", a tragic story in which the prince of India's Magadha kingdom murders his father, the king, to usurp the throne. The 16 subsequent scenes show $S{\bar{a}}kyamuni$ teaching the distressed queen how a person can be reborn in paradise through meditation and praying. In the kwan-gyongdo in the Mogao Cave No. 17 in Dunhuang, China, painted during the Tang dynasty (618-907), the preface and the 16 scenes are presented in one painting, whereas they are presented in two paintings in those painted in Korea during the Koryo period (918-1392). The difference is attributed to the stylistic disparity of the two periods. Despite the temporal gap between the Koryo paintings and the Mogao Cave paintings, a comparison of the two can show the characteristic development of kwan-gyongdo. Kwan-gyongdo of the Koryo period do not have the "enmity created in the previous life" scene featuring a heavenly figure and a hare, a result that shows the influence of the Tang school that deleted the scene. The scene of $S{\bar{a}}kyamuni$ preaching on the Mountain of Spirit is included in kwan-gyongdo of both the Koryo period and the Mogao Cave, but the scene of $S{\bar{a}}kyamuni$ emerging from the earth to the Magadha palace is not included in Koryo kwan-gyongdo. Kwan-gyongdo of Koryo are generally a simpler but more faithful rendering of the $s{\bar{u}}tra$.

• Convergence Security Journal
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• v.18 no.2
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• pp.33-40
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• 2018

ICT technology has been applied to various educational fields, but applying to educational test field is limited. Computer-based test (CBT) can overcome temporal and spatial constraints of conventional paper-based test, but is vulnerable to fraud by test parties. In this paper, we propose real-time monitoring and process management methods to enhance the security of CBT. In the proposed methods, the test screens of students are periodically captured and transferred to the professor screen to enable real-time monitoring, and the possible processes used for cheating can be blocked before testing. In order to monitor the screen of many students in real time, effective compression of the captured original image is important. We applied three-step compression methods: initial image compression, resolution reduction, and re-compression. Through this, the original image of about 6MB was converted into the storage image of about 3.8KB. We use the process extraction and management functions of Windows API to block the processes that may be used for cheating. The CBT system of this paper with the new security enhancement methods shows the superiority through comparison of the security related functions with the existing CBT systems.

• Tunnel and Underground Space
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• v.30 no.6
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• pp.519-539
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• 2020

We reviewed numerical approaches to assess a hydraulic properties of excavation-disturbed zone (EDZ)created in argillaceous sedimentary rocks. It has been reported that fractures in the sedimentary rocks containing expansive clays are gradually closing due to swelling and their permeabilities are evolving to the level of in-tact rock, which is known as a self-healing or self-sealing process. The numerical approaches introduced here are capable of simulating spatio-temporal variation of EDZ permeability during long-term operation of a repository by including the self-healing characteristics of fractures, which wa observed in laboratory as well as in-situ experiments, The applicability of the numerical approaches was verified from the comparison to in-situ measurements of EDZ permeability at underground research laboratories.

• Journal of Korean Society on Water Environment
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• v.27 no.3
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• pp.273-285
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• 2011

This study describes the modeling of climate change impact on runoff across southeast Korea using a conceptual rainfall-runoff model TANK and assesses the results using the concept of environmental flows developed by International Water Management Institute. The future climate time series is obtained by scaling the historical series, informed by 4 global climate models and 3 greenhouse gas emission scenarios, to reflect a $4.0^{\circ}C$ increase at most in average surface air temperature and 31.7% increase at most in annual precipitation, using the spatio-temporal changing factor method that considers changes in the future mean seasonal rainfall and potential evapotranspiration as well as in the daily rainfall distribution. Although the simulation results from different global circulation models and greenhouse emission scenarios indicate different responses in flows to the climate change, the majority of the modeling results show that there will be more runoff in southeast Korea in the future. However, there is substantial uncertainty, with the results ranging from a 5.82% decrease to a 48.15% increase in the mean annual runoff averaged across the study area according to the corresponding climate change scenarios. We then assess the hydrologic perturbations based on the comparison between present and future flow duration curves suggested by IMWI. As a result, the effect of hydrologic perturbation on aquatic ecosystems may be significant at several locations of the Nakdong river main stream in dry season.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.180-191
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• 2020

This study analyzes factors that affect average daily travel time budgets, using the Time Use Survey data from 1999 to 2014 in Seoul. We first developed multivariate regression models for travel time from each year, considering demographic and socio-economic variables as well as non-home activity time. The model results showed that household and personal characteristics and non-home activities significantly affect travel time, and their effects are different over time. In addition, we developed seemingly unrelated regression (SUR) models for time allocation for non-home activity and travel, considering their correlations, and explanatory variables were compared over time. Overall, demographic and socio-economic variables significantly affect travel time as well as non-home activity time.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.162-169
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• 2022

In a dynamically changing time-varying network environment, the optimal moving pattern of edge devices can be applied to distributing computing resources to edge cloud servers or deploying new edge servers in the FEC(Fog/Edge Computing) environment. In addition, this can be used to build an environment capable of efficient computation offloading to alleviate latency problems, which are disadvantages of cloud computing. This paper proposes an algorithm to extract the optimal moving pattern by analyzing the moving path of multiple edge devices requiring application services in an arbitrary spatio-temporal environment based on frequency. A comparative experiment with A^* and Dijkstra algorithms shows that the proposed algorithm uses a relatively fast execution time and less memory, and extracts a more accurate optimal path. Furthermore, it was deduced from the comparison result with the A^* algorithm that applying weights (preference, congestion, etc.) simultaneously with frequency can increase path extraction accuracy.

• Journal of The Korean Society of Integrative Medicine
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• v.10 no.3
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• pp.233-246
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• 2022

Purpose : The study aims to determine the effects of virtual and non-virtual realities in a normal person's mirror walk on gait characteristics. Methods : Twenty male adults (Age: 27.8 ± 5.8 years) participated in the study. Reflection markers were attached to the subjects for motion analysis, and they walked in virtual reality environments with mirrors by wearing goggles that showed them the virtual environments. After walking in virtual environments, the subjects walked in non-virtual environments with mirrors a certain distance away after taking a 5 min break. To prevent the order effect caused by the experiential difference of gait order, the subjects were randomly classified into groups of 10 and the order was differentiated. During each walk, an infrared camera was used to detect motion and the marker positions were saved in real time. Results : Comparison between the virtual and non-virtual reality mirror walks showed that the movable range of the leg joints (ankle, knee, and hip joints), body joints (sacroiliac and atlantoaxial joints), and arm joints (shoulder and wrist joints) significantly differed. Temporal characteristics showed that compared to non-virtual gaits, the virtual gaits were slower and the cycle time and double limb support time of virtual gaits were longer. Furthermore, spacial characteristics showed that compared to non-virtual gaits, virtual gaits had shorter steps and stride lengths and longer stride width and horizontally longer center of movement. Conclusion : The reduction in the joint movement in virtual reality compared to that in non-virtual reality is due to adverse effects on balance and efficiency during walking. Moreover, the spatiotemporal characteristics change based on the gait mechanisms for balance, exhibiting that virtual walks are more demanding than non-virtual walks. However, note that the subject group is a normal group with no abnormalities in gait and balance and it is unclear whether the decrease in performance is due to the environment or fear. Therefore, the effects of the subject group's improvement and fear on the results need to be analyzed in future studies.

• Atmosphere
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• v.33 no.4
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• pp.399-411
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• 2023

This study compares the three detection algorithms of East Asian summer atmospheric rivers (ARs). The algorithms developed by Guan and Waliser (GW15), Park et al. (P21), and Tian et al. (T23) are particularly compared in terms of the AR frequency, the number of AR events, and the AR duration for the period of 2016-2020. All three algorithms show similar spatio-temporal distributions of AR frequency, centered along the edge of the North Pacific high. The maximum AR frequency gradually shifts northward in early summer as the edge of the North Pacific High expands, and retreats in late summer. However, the detailed pattern and the maximum value differ among the algorithms. When the AR frequency is decomposed into the number of AR events and the AR duration, the AR frequencies detected by GW15 and P21 are equally explained by both factors. However, the number of AR events primarily determine the AR frequency in T23. This difference occurs as T23 utilizes the machine learning algorithm applied to moisture field while GW15 and P21 apply the threshold value to moisture transport field. When evaluating AR-related precipitation, the ARs detected by P21 show the closest relationship with total precipitation in East Asia by up to 60%. These results indicate that AR detection in the East Asian summer is sensitive to the choice of the detection algorithm and can be optimized for the target region.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.67-75
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• 2023

Stock price prediction is an important topic extensively discussed in the financial market, but it is considered a challenging subject due to numerous factors that can influence it. In this research, performance was compared and analyzed by applying time series prediction models (LSTM, GRU) and non-time series prediction models (RF, SVR, KNN, LGBM) that do not take into account the temporal dependence of data into stock price prediction. In addition, various data such as stock price data, technical indicators, financial statements indicators, buy sell indicators, short selling, and foreign indicators were combined to find optimal predictors and analyze major factors affecting stock price prediction by industry. Through the hyperparameter optimization process, the process of improving the prediction performance for each algorithm was also conducted to analyze the factors affecting the performance. As a result of feature selection and hyperparameter optimization, it was found that the forecast accuracy of the time series prediction algorithm GRU and LSTM+GRU was the highest.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1530-1544
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• 2023

Data monitoring is an important foundation of modern science. In most cases, the monitoring data is time-series data, which has high application value. The deep learning algorithm has a strong nonlinear fitting capability, which enables the recognition of time series by capturing anomalous information in time series. At present, the research of time series recognition based on deep learning is especially important for data monitoring. Deep learning algorithms require a large amount of data for training. However, abnormal sample is a small sample in time series, which means the number of abnormal time series can seriously affect the accuracy of recognition algorithm because of class imbalance. In order to increase the number of abnormal sample, a data augmentation method called GANBATS (GAN-based Bi-LSTM and Attention for Time Series) is proposed. In GANBATS, Bi-LSTM is introduced to extract the timing features and then transfer features to the generator network of GANBATS.GANBATS also modifies the discriminator network by adding an attention mechanism to achieve global attention for time series. At the end of discriminator, GANBATS is adding averagepooling layer, which merges temporal features to boost the operational efficiency. In this paper, four time series datasets and five data augmentation algorithms are used for comparison experiments. The generated data are measured by PRD(Percent Root Mean Square Difference) and DTW(Dynamic Time Warping). The experimental results show that GANBATS reduces up to 26.22 in PRD metric and 9.45 in DTW metric. In addition, this paper uses different algorithms to reconstruct the datasets and compare them by classification accuracy. The classification accuracy is improved by 6.44%-12.96% on four time series datasets.