• The Journal of Korean society of community based occupational therapy
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• v.4 no.1
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• pp.75-84
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• 2014

Objective : This study intends to suggest an effective clinical intervention method for stroke patients by applying a trunk stabilization training program to a single stroke patient and investigating and analyzing the effects of the program on postural control and activities of daily living. Methods : The subject of this study was a 75 year-old female stroke patient hospitalized in C Hospital located in Gyeongju, Gyeongsangbuk-do. As for the research design, A-B-A reversal design was used with a single subject design; and the research period was a total of five weeks from April 21, 2014 to May 23, 2014. As for the research process, a total of 15 sessions were carried out including three sessions of baseline(A), nine sessions of an intervention period(B), and three sessions of maintenance(A'); and the trunk stabilization program was applied during the intervention period. To evaluate the participant's postural control and activities of living life, the Postural Assessment Scale for Stroke(PASS) and the modified Barthel Index(MBI) were used. Visual analysis was used in which collected data were plotted on a graph, using Microsoft Office Excel 2013. Results : In the postural control test, the mean scores improved from 11.7 in the baseline phase to 14.8 in the intervention phase and 15.3 in the maintenance phase. In the evaluation of activities of daily living, the score improved from 66(medium dependence) during the baseline period to 80(low dependence) after intervention and at the maintenance phase. Conclusion : It was found that the trunk stabilization training program showed effects on the stroke patient's postural control or alignment, and furthermore a beneficial improvement in the patient's performing the activities of daily living, as well.

• Progress in Medical Physics
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• v.20 no.4
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• pp.244-252
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• 2009

The purpose of this study is to compensate for susceptibility and a ferromagnetic body artifact using CFA and TGV on MR Imaging. A total of 30 patients (15 men and 15 women, mean age: 45 years) were performed on head and neck diseases. MR Unit used a 1.5T superconducting magnet (GE medical system, High Density). This study have investigated by changing with CFA and TGV (70, 90, 110, 130, 150) searching for compensation values about susceptibility and a ferromagnetic body artifact in 60 kg standards of body weight (p<0.05). As a quality results, Image qualities were obtained at different score from CFA and TGV (70, 90, 110, 130, $150=3.23{\pm}0.35$, $4.31{\pm}0.02$ $4.23{\pm}0.21$, $5.12{\pm}0.25$, $7.13{\pm}0.72$, $8.31{\pm}0.01$, $5.21{\pm}0.15$, $6.14{\pm}0.08$, $5.23{\pm}0.72$, $5.91{\pm}0.06$, p<0.05). Absolute CNRs (TG, CNRpre, CNRpost) were acquired with (70:$-1.44{\pm}0.11$, $-2.7{\pm}0.04$, 90:$-2.18{\pm}0.42$, $-4.41{\pm}0.43$, 110:$-2.89{\pm}0.43$, $-5.23{\pm}0.02$, 130:$-2.34{\pm}0.05$, $-5.26{\pm}0.01$, 150: $-2.09{\pm}0.08$, $-3.87{\pm}0.12$, p<0.05). In conclusions, this study could be compensated for metal and flow artifacts surrounding the tissues having artifact by changing CFA and TGV.

• Journal of Korean Orthopaedic Sports Medicine
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• v.9 no.2
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• pp.98-103
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• 2010

Purpose: We reported the results of arthroscopically assisted lateral release and medial imbrication for the recurrent patella dislocation. Materials and Methods: Twenty patients (20 knees) underwent arthroscopically assisted surgery for the recurrent patella dislocation. There were 4 males and 16 female. The average age was 20.2 years. All patients had definite trauma history and average follow-up period was 19 months. The surgical results were evaluated according to the Lysholm knee score and the Kujala score. The congruence angle and lateral patellofemoral angle were measured on plain radiograph and the tibial tubercle-trochlear groove distance was calculated on computerized tomography. Results: The median value of preoperative congruence angle was $16.5^{\circ}$ (range, $0.0{\sim}+34^{\circ}$) and the average final follow-up was $-6.4^{\circ}$ (range, $-19{\sim}10^{\circ}$) with statistically significant improvement (p=0.025). The median value of preoperative Lysholm knee score was 70 (range, 63~81) and the final follow-up score had changed to 88 (range, 80~95) with statistically significant improvement (p=0.0341). The median value of preoperative Kujala score was 72 (range, 65~80) and the average final follow-up score showed 87 (range, 80~92) with statistically significant improvement (p=0.024). Recurrent dislocations after surgery occurred in 2 cases, one case which showed positive "thumb to forearm test" had been treated with medial patellofemoral ligament reconstruction. Conclusion: Arthroscopically assisted lateral release and medial imbrication for recurrent patella dislocation without bony malaligmenent showed the effective treatment, but would be inappropriate for the patients with the generalized joint laxity.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.