• Fisheries and Aquatic Sciences
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• v.24 no.7
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• pp.243-259
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• 2021

The spatial and temporal behaviors and fluctuations of the cold water that appeared in the South East Sea and the East Sea coast from 2016 to 2017 were investigated. The water temperature drop was large in the east coast from April to June and the southeast coast from July to September, and the temperature drop period was longer in the southeast coast. The water temperature fluctuated sensitively to the wind direction, and it gradually decreased in the southwest wind but rose as if jumping in the northeast wind. Wind stress and surface water temperature had an inverse correlation, which was larger in Bukhang-Idukseo, and decreased toward the north of Guryongpo. The cold water appeared mainly in Geojedo-Pohang after 1 to 2 days when the southwest wind was strong, but when the wind became weak, it shrank to the Idukseo (Ulgi-Gampo) and extended into the open sea in a tongue shape. Cold water was distributed only in Samcheok-Toseong in mid-May, Idukseo-Guryongpo and Hupo-Jukbyeon-Samcheok from late May to mid-July, and Bukhang-Idukseo in August-September. The intensity of cold water was greatest in mid-August, and the center of cold water descended from the east coast to the southeast coast from spring to summer. The water temperature fluctuation was dominant at the periods of 1 d and 7-21 d. In wavelet spectrum analysis of water temperature and wind, wind speed increase-water temperature decrease showed phase difference of 12 h in 2 d, 18 h in 3 d, 1.5 d in 4-8 d, and 2-3 d in 8-24 d period. The correlation between the two parameters was large in Geojedo and Namhang, Bukhang-Idukseo, Guryongpo-Jukbyeon, and Samcheok-Toseong. Monitoring stations with high correlation in all periods were generally parallel to the monsoon direction.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.54 no.6
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• pp.989-999
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• 2021

The sound of finless porpoises Neophocaena asiaeorientalis was recorded with an acoustic recorder to confirm their emergence in the South Sea of South Korea in February, June, and November 2020. Sea water temperature and salinity were also measured. In addition, a sighting survey was conducted to observe the behavior of the finless porpoises and the marine environment, and the clicks of the finless porpoises were recorded every day. The results showed that they always emerged in the survey area. The finless porpoises mainly foraged, whereas some played or rested. The water temperature range of areas where the finless porpoises emerged was 7.5-23.5℃. Assuming that the number of clicks corresponds to the number of finless porpoises, the finless porpoises emerged the most during spring. The emergence decreased during winter and was the lowest during autumn. The finless porpoises emerged more during the daytime than during the nighttime in all seasons, indicating a temporal difference in the usage of the survey area. This might be due to the movement of prey organisms according to regional characteristics. A long-term survey and research on habitat use and environment is needed to manage and conserve the finless porpoises.

• Clinical Pain
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• v.19 no.1
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• pp.8-15
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• 2020

Objective: To compare the therapeutic efficacy of the bone marrow aspirate concentrate (BMAC)- platelet-rich plasma (PRP) complex with hyaluronic acid in patients with knee osteoarthritis. Method: Thirty-four patients with knee osteoarthritis participated in this study. Seventeen patients in the study group underwent BMAC and PRP extraction followed by intra-articular injection of BMAC-PRP complex within affected knee. Seventeen patients in the control group underwent intra-articular injection of hyaluronic acid. Knee injury, osteoarthritic outcome score (KOOS), and EuroQol-5D (EQ-5D) questionnaire were evaluated before, one month, three months, and six months after the injection. Results: There were statistically significant temporal differences in total KOOS scores in both BMAC-PRP and HA groups. However, there were no significant group difference in the study period. In the Sports and Recreational Function Scale, there was statistically significant improvement in the BMAC-PRP group compared to the HA group at three months (p=0.041). There were no side effects or complications in both groups. Conclusion: Intra-articular injection of BMAC-PRP showed better functional recovery in the OA at three months and this can be an alternative treatment in terms of functional recovery in the OA in addition to the decrease of pain.

• 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 Periodontal and Implant Science
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• v.53 no.4
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• pp.248-258
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• 2023

Purpose: This study aimed to characterize the early stages of periodontal disease and determine the optimal period for its evaluation in a mouse model. The association between the duration of ligation and its effect on the dentogingival area in mice was evaluated using micro-computed tomography (CT) and histological analysis. Methods: Ninety mice were allocated to an untreated control group or a ligation group in which periodontitis was induced by a 6-0 silk ligation around the left second maxillary molar. Mice were sacrificed at 1, 2, 3, 4, 5, 8, 11, and 14 days after ligature placement. Alveolar bone destruction was evaluated using micro-CT. Histological analysis was performed to assess the immune-inflammatory processes in the periodontal tissue. Results: No significant difference in alveolar bone loss was found compared to the control group until day 3 after ligature placement, and a gradual increase in alveolar bone loss was observed from 4 to 8 days following ligature placement. No significant between-group differences were observed after 8 days. The histological analysis demonstrated that the inflammatory response was evident from day 4. Conclusions: Our findings in a mouse model provide experimental evidence that ligature-induced periodontitis models offer a consistent progression of disease with marginal attachment down-growth, inflammatory infiltration, and alveolar bone loss.

• 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.

• Journal of People, Plants, and Environment
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• v.22 no.2
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• pp.127-143
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• 2019

This study investigates the color stimuli of two varieties of foliage plants by extracting electroencephalogram, electrocardiogram and physiology activity data from 30 participants in their 50s or older. Changes in the physiological activity of subjects against six color stimuli were examined. The stimulus to real green plants 'Silver Queen' was set as the control group, and was compared with other groups including the stimulus to real 'Angel' plants and four stimuli to artificial colors (two color images and color schemes of the same green and red plants). Compared to the five groups, the relative theta power spectrum (RT) and the ratio of alpha to high beta (RAHB) increased in the subjects exposed to real green plants. This result demonstrates that the green plant ('Silver Queen') increases the stability, relaxation, and internal concentration of subjects in a proper state of awakening. The result of this experiment showed a statistically significant difference in the level of RT when subjects were exposed to the groups of real green and red plants. This finding indicates that the green plant increases internal concentration more than the red plant. RT and the relative low beta power spectrum (RLB) in the groups of natural colors were higher than the groups of artificial colors when subjects focused their mind on the two types of real plants. However, the level of relative mid beta power spectrum (RMB), ratio of SMR to theta (RST), ratio of mid beta to theta (RMT), relative high beta power spectrum (RHB), and spectral edge frequency 95% were higher when subjects were exposed to the photos and colors scheme of plants than when they were exposed to real plants. The subjects experienced more "comfortable" emotions when they were looking at plants with green colors. Overall, it is recommended to use the natural colors of real plants in places where which stability and relaxation are required. On the contrary, the artificial colors of plants such as their photos and color schemes are useful in places where a high level of concentration is required in a short period of time.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.57-69
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• 2024

Crop classification plays a vitalrole in monitoring agricultural landscapes and enhancing food production. In this study, we explore the effectiveness of Long Short-Term Memory (LSTM) models for crop classification, focusing on distinguishing between apple and rice crops. The aim wasto overcome the challenges associatedwith finding phenology-based classification thresholds by utilizing LSTM to capture the entire Normalized Difference Vegetation Index (NDVI)trend. Our methodology involvestraining the LSTM model using a reference site and applying it to three separate three test sites. Firstly, we generated 25 NDVI imagesfrom the Sentinel-2A data. Aftersegmenting study areas, we calculated the mean NDVI values for each segment. For the reference area, employed a training approach utilizing the NDVI trend line. This trend line served as the basis for training our crop classification model. Following the training phase, we applied the trained model to three separate test sites. The results demonstrated a high overall accuracy of 0.92 and a kappa coefficient of 0.85 for the reference site. The overall accuracies for the test sites were also favorable, ranging from 0.88 to 0.92, indicating successful classification outcomes. We also found that certain phenological metrics can be less effective in crop classification therefore limitations of relying solely on phenological map thresholds and emphasizes the challenges in detecting phenology in real-time, particularly in the early stages of crops. Our study demonstrates the potential of LSTM models in crop classification tasks, showcasing their ability to capture temporal dependencies and analyze timeseriesremote sensing data.While limitations exist in capturing specific phenological events, the integration of alternative approaches holds promise for enhancing classification accuracy. By leveraging advanced techniques and considering the specific challenges of agricultural landscapes, we can continue to refine crop classification models and support agricultural management practices.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.891-907
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• 2023

Compact Advanced Satellite 500-4 (CAS500-4) is scheduled to be launched to collect high spatial resolution data focusing on vegetation applications. To achieve this goal, accurate surface reflectance retrieval through atmospheric correction is crucial. Therefore, a machine learning-based atmospheric correction algorithm was developed to simulate atmospheric correction from a radiative transfer model using Sentinel-2 data that have similarspectral characteristics as CAS500-4. The algorithm was then evaluated mainly for forest areas. Utilizing the atmospheric correction parameters extracted from Sentinel-2 and GEOKOMPSAT-2A (GK-2A), the atmospheric correction algorithm was developed based on Random Forest and Light Gradient Boosting Machine (LGBM). Between the two machine learning techniques, LGBM performed better when considering both accuracy and efficiency. Except for one station, the results had a correlation coefficient of more than 0.91 and well-reflected temporal variations of the Normalized Difference Vegetation Index (i.e., vegetation phenology). GK-2A provides Aerosol Optical Depth (AOD) and water vapor, which are essential parameters for atmospheric correction, but additional processing should be required in the future to mitigate the problem caused by their many missing values. This study provided the basis for the atmospheric correction of CAS500-4 by developing a machine learning-based atmospheric correction simulation algorithm.