• Physical Therapy Rehabilitation Science
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• v.6 no.3
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• pp.106-112
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• 2017

Objective: Robot assisted gait training is implemented as part of therapy for the recovery of gait patterns in recent clinical fields, and the scope of implications are continuously increasing. However clear therapy protocols of robot assisted gait training are insufficent. The purpose of this study was to investigate the effects of robot-assisted gait training applied with guidance force on balance and gait performance in persons with hemiparetic stroke. Design: Two group pre-test post-test design. Methods: Nineteen persons were diagnosed with hemiparesis following stroke participated in this study. The participants were randomly assigned to the unilateral guidance group or bilateral guidance group to conduct robot-assisted gait training. All participants underwent robot-assisted gait training for twelve sessions (30 min/d, 3 d/wk for 4 weeks). They were assessed with gait parameters (gait velocity, cadence, step length, stance phase, and swing phase) using Optogait. This study also measured the dynamic gait index (DGI), the Berg balance scale (BBS) score, and timed up and go (TUG). Results: After training, BBS scores were was significantly increased in the bilateral training group than in the unilateral guidance group (p<0.05). Spatiotemporal parameters were significantly changed in the bilateral training group (gait speed, swing phase ratio, and stance phase ratio) compared to the unilateral training group (p<0.05). Conclusions: The results of this study suggest that robot-assisted gait training show feasibility in facilitating improvements in balance and gait performance for subacute hemiparetic stroke patients.

• Atmosphere
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• v.20 no.4
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• pp.415-426
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• 2010

A Land cover map over East Asian region (Kongju national university Land Cover map: KLC) is classified by using support vector machine (SVM) and evaluated with ground truth data. The basic input data are the recent three years (2006-2008) of MODIS (MODerate Imaging Spectriradiometer) NDVI (normalized difference vegetation index) data. The spatial resolution and temporal frequency of MODIS NDVI are 1km and 16 days, respectively. To minimize the number of cloud contaminated pixels in the MODIS NDVI data, the maximum value composite is applied to the 16 days data. And correction of cloud contaminated pixels based on the spatiotemporal continuity assumption are applied to the monthly NDVI data. To reduce the dataset and improve the classification quality, 9 phenological data, such as, NDVI maximum, amplitude, average, and others, derived from the corrected monthly NDVI data. The 3 types of land cover maps (International Geosphere Biosphere Programme: IGBP, University of Maryland: UMd, and MODIS) were used to build up a "quasi" ground truth data set, which were composed of pixels where the three land cover maps classified as the same land cover type. The classification results show that the fractions of broadleaf trees and grasslands are greater, but those of the croplands and needleleaf trees are smaller compared to those of the IGBP or UMd. The validation results using in-situ observation database show that the percentages of pixels in agreement with the observations are 80%, 77%, 63%, 57% in MODIS, KLC, IGBP, UMd land cover data, respectively. The significant differences in land cover types among the MODIS, IGBP, UMd and KLC are mainly occurred at the southern China and Manchuria, where most of pixels are contaminated by cloud and snow during summer and winter, respectively. It shows that the quality of raw data is one of the most important factors in land cover classification.

• Journal of Korean Society of Transportation
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• v.35 no.6
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• pp.525-544
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• 2017

With the advent of the big data era, the interest in the development of land using traffic data has increased significantly. However, the current research on traffic big data lingers around organizing or calibrating the data only. In this research, a novel method for discovering the hidden values within the traffic data through data mining is proposed. Considering the fact that traffic data and network structures have similarities, network analysis algorithms are used to find valuable information in the actual traffic volume data. The PageRank and HITS algorithms are then employed to find the centralities. While conventional methods present centralities based on uncomplicated traffic volume data, the proposed method provides more reasonable centrality locations through network analysis. Since the centrality locations that we have found carry detailed spatiotemporal characteristics, such information can be used as an objective basis for making policy decisions.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.44 no.6
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• pp.717-731
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• 2011

To clarify relationships between the abundance and biological characteristics of Pacific salmon Oncorhynchus spp., we analyzed spatiotemporal changes in fork length, body weight, and an index of relative abundance (catch per unit effort, CPUE) for pink salmon (O. gorbuscha), chum salmon (O. keta), and sockeye salmon (O. nerka) collected by research gill-nets from the T/V Oshoro-maru and the T/V Hokusei-maru of Hokkaido University in the North Pacific during 1953-2007. Populations of each species were distributed throughout the western Bering Sea, eastern Bering Sea (EB), western North Pacific (WNP), central North Pacific (CNP), eastern North Pacific (ENP), and Okhotsk Sea. Since 1970, the average body size of chum salmon at ocean ages 0.3-0.4 has generally declined in the WNP and CNP. However, the average body sizes of sockeye and pink salmon have not shown temporal changes. Chum salmon showed significant negative (positive) correlations between CPUE and body size for populations in CNP (ENP) at ocean ages 0.2-0.3 (age 0.1) for both sexes. In general, sockeye salmon also showed significant negative (positive) correlations between CPUE and body size for populations in the EB at ocean ages X.2-X.3 (age X.1) for both sexes, except in CNP at age 2. Our results suggest that better growth by chum and sockeye salmon in the early periods of their ocean life histories might produce higher abundance. This higher abundance, which might also be affected by overlapping distributions among Pacific salmon species and populations in certain seas, in turn appears to cause density-dependent declines in growth in the following ocean life history period due to the limited carrying capacity of the seas. To understand complex dynamics in Pacific salmon species in the North Pacific Ocean, research on interactions among species and populations is needed.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.260-260
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• 2017

This study aims to compare the performance of TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope-stability model) and TiVaSS (Time-variant Slope Stability model) in the prediction of rainfall-induced shallow landslides. TRIGRS employs one-dimensional (1-D) subsurface flow to simulate the infiltration rate, whereas a three-dimensional (3-D) model is utilized in TiVaSS. The former has been widely used in landslide modeling, while the latter was developed only recently. Both programs are used for the spatiotemporal prediction of shallow landslides caused by rainfall. The present study uses the July 2011 landslide event that occurred in Mt. Umyeon, Seoul, Korea, for validation. The performance of the two programs is evaluated by comparison with data of the actual landslides in both location and timing by using a landslide ratio for each factor of safety class ( index), which was developed for addressing point-like landslide locations. In addition, the influence of surface flow on landslide initiation is assessed. The results show that the shallow landslides predicted by the two models have characteristics that are highly consistent with those of the observed sliding sites, although the performance of TiVaSS is slightly better. Overland flow affects the buildup of the pressure head and reduces the slope stability, although this influence was not significant in this case. A slight increase in the predicted unstable area from 19.30% to 19.93% was recorded when the overland flow was considered. It is concluded that both models are suitable for application in the study area. However, although it is a well-established model requiring less input data and shorter run times, TRIGRS produces less accurate results.

• Journal of Oriental Neuropsychiatry
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• v.31 no.4
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• pp.225-233
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• 2020

Objectives: This study aimed to reveal the gait characteristics of each Sasang constitution by examining the differences in gait analysis indicators using a 3-axis accelerometer. Methods: Ninety-one participants were classified through the TS-QSCD (Two-Step Questionnaire for Sasang Constitution Diagnosis) method and gait analysis was performed using a 3-Axis Accelerometer (G-WALK. BTS Bioengineering, Italy). Gait analysis in returning to the 6-meter turnaround point and 6-minute walking test were performed. The differences in the gait analysis index values were analyzed between each constitution. Results: The gait analysis of 91 subjects (37 Taeumin, 37 Soyangin, and 17 Soeumin), showed that the percent stride length/height in the Soyangin subjects was significantly higher than that of the Taeeumin and Soeuminin subjects in the spatiotemporal walking variables (p<0.05). Stride length also showed the widest tendency in the Soyangin subjects (p=0.05). In the kinesiological analysis, the range of pelvic obliquity angles in the Soeumin subjects was significantly wider than that of the Taeumin and Soyangin subjects (p<0.05). In the six-minute walking test, the Soyangin subjects walked the farthest at 309.41±35.23 m (p=0.064). Conclusions: In a comparison of the gait characteristics for each Sasang constitution using a three-dimensional accelerometer, the stride width of the Soyangin subjects was the widest compared to the Taeeumin, and Soeumin subjects, and Soyangin's walking speed showed a faster tendency than that of the Taeeumin and Soeumin subjects.

• Physical Therapy Korea
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• v.30 no.2
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• pp.110-119
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• 2023

Background: Osteoarthritis is a common condition with an increasing prevalence and is a common cause of disability. Osteoarthritic pain decreases the quality of life, and simple gait training is used to alleviate it. Knee osteoarthritis limits joint motion in the sagittal and lateral directions. Although many recent studies have activated orthotic research to increase knee joint stabilization, no study has used patellar tendon straps to treat knee osteoarthritis. Objects: This study aimed to determine the effects of patellar tendon straps on kinematic, mechanical, and electromyographic activation in patients with knee osteoarthritis. Methods: Patients with knee osteoarthritis were selected. After creating the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), leg length difference, Q-angle, and thumb side flexion angle of the foot were measured. Kinematic, kinetic, and muscle activation data during walking before and after wearing the orthosis were viewed. Results: After wearing the patellar tendon straps, hip adduction from the terminal stance phase, knee flexion from the terminal swing phase, and ankle plantar flexion angle increased during the pre-swing and initial swing phases. The cadence of spatiotemporal parameters and velocity increased, and step time, stride time, and foot force duration decreased. Conclusion: Based on the results of this study, the increase in plantar flexion after strap wearing is inferred by an increase due to neurological mechanisms, and adduction at the hip joint is inferred by an increase in adduction due to increased velocity. The increase in cadence and velocity and the decrease in gait speed and foot pressure duration may be due to joint stabilization. It can be inferred that joint stabilization is increased by wearing knee straps. Thus, wearing a patellar tendon strap during gait in patients with knee osteoarthritis influences kinematic changes in the sagittal plane of the joint.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1109-1123
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• 2020

In South Korea with forest as a major land cover class (over 60% of the country), many wildfires occur every year. Wildfires weaken the shear strength of the soil, forming a layer of soil that is vulnerable to landslides. It is important to identify the severity of a wildfire as well as the burned area to sustainably manage the forest. Although satellite remote sensing has been widely used to map wildfire severity, it is often difficult to determine the severity using only the temporal change of satellite-derived indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR). In this study, we proposed an approach for determining wildfire severity based on machine learning through the synergistic use of Sentinel-1A Synthetic Aperture Radar-C data and Sentinel-2A Multi Spectral Instrument data. Three wildfire cases-Samcheok in May 2017, Gangreung·Donghae in April 2019, and Gosung·Sokcho in April 2019-were used for developing wildfire severity mapping models with three machine learning algorithms (i.e., Random Forest, Logistic Regression, and Support Vector Machine). The results showed that the random forest model yielded the best performance, resulting in an overall accuracy of 82.3%. The cross-site validation to examine the spatiotemporal transferability of the machine learning models showed that the models were highly sensitive to temporal differences between the training and validation sites, especially in the early growing season. This implies that a more robust model with high spatiotemporal transferability can be developed when more wildfire cases with different seasons and areas are added in the future.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1031-1042
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• 2023

In this study, a deep learning model was developed to predict the yield of cabbage and radish, one of the five major supply and demand management vegetables, using satellite images of Landsat 8. To predict the yield of cabbage and radish in Gangwon-do from 2015 to 2020, satellite images from June to September, the growing period of cabbage and radish, were used. Normalized difference vegetation index, enhanced vegetation index, lead area index, and land surface temperature were employed in this study as input data for the yield model. Crop yields can be effectively predicted using satellite images because satellites collect continuous spatiotemporal data on the global environment. Based on the model developed previous study, a model designed for input data was proposed in this study. Using time series satellite images, convolutional neural network, a deep learning model, was used to predict crop yield. Landsat 8 provides images every 16 days, but it is difficult to acquire images especially in summer due to the influence of weather such as clouds. As a result, yield prediction was conducted by splitting June to July into one part and August to September into two. Yield prediction was performed using a machine learning approach and reference models , and modeling performance was compared. The model's performance and early predictability were assessed using year-by-year cross-validation and early prediction. The findings of this study could be applied as basic studies to predict the yield of field crops in Korea.

• Journal of the Korean Geographical Society
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• v.48 no.4
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• pp.497-508
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• 2013

The objective of this study is to determine the spatiotemporal influences of climatic factors and atmospheric aerosol on phenological cycles of the Korea Peninsular on a regional scale. High temporal-resolution satellite data can overcome limitations of ground-based phenological studies with reasonable spatial resolution. Study results showed that phenological characteristics were similar among evergreen forest, deciduous forest, and grassland, while the inter-annual vegetation index amplitude of mixed forest was differentiated from the other forest types. Forest types with high VI amplitude reached their maximum VI values earlier, but this relationship was not observed within the same forest type. The phase of VI, or the peak time of greenness, was significantly influenced by air temperature. Aerosol optical thickness (AOT) time-series showed strong seasonal and inter-annual variations. Generally, aerosol concentrations were peaked during late spring and early summer. However, inter-annual AOT variations did not have significant relationships with those of VIs. Weak relationships between AOT amplitude and EVI amplitude only indicates that there would be potential impacts of aerosols on vegetation growth in the long run.