• Radiation Oncology Journal
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• v.18 no.2
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• pp.114-119
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• 2000

Purpose : To evaluate the e지ent and frequency of the inter- and intra-treatment isocenter deviations of the whole pelvis radiation field in using small bowel displacement system (SBDS). Methods and Materials : Using electronic portal imaging device (EPID), 302 postero-anterior 232 lateral portal images were prospectively collected from 11 patients who received pelvic radiation therapy (7 with cervix cancer and 4 with rectal cancer). All patients were treated in prone position with SBDS under the lower abdomen. Five metallic fiducial markers were placed on the image detection unit for the recognition of the isocenter and magnification. After aligning the bony landmarks of the EPID images on those of the reference image, the deviations of the isocenter were measured in right-left (RL), cranio-caudal (CC), and PA directions. Results : The mean inter-treatment deviation of the isocenter in each RL, CC, and PA direction was 1.2 mm ($\pm$ 1.6 mm), 1.0 mm ($\pm$3.0 mm), and 0.9 mm ($\pm$4.4 mm), respectively. Inter-treatment isocenter deviations over 5 mm and 10 mm in RL, CC, and PA direction were 2, 12, 24$\%$, and 0, 0, 5$\%$, respectively. Maximal deviation was detected in PA direction, and was 11.5 mm. The mean intratreatment deviation of the isocenter in RL, CC, and PA direction was 0 mm ($\pm$0.9 mm), 0.1 mm ($\pm$ 1.9mm), and 0 mm ($\pm$1.6 mm), respectively. All intra-treatment isocenter deviations over 5 mm in each direction were 0, 1, 1$\pm$, respectively. Conclusions : As the greatest and the most frequent inter-treatment deviation of the isocenter was along the PA direction, it is recommended to put more generous safety margin toward the PA direction on the lateral fields if clinically acceptable in pelvic radiotherapy with SBDD.

• 한국노년학
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• v.37 no.1
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• pp.237-250
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• 2017

The purpose of this study is to analyze the effects of exercise program for prevention of falling on physical fitness, posture and fall prevention self-efficacy for elderly women. 30 females above the age of 65 were subjects for this study. Over an twelve week period, 14women in the experimental group performed exercise 2 times a week for 60 minutes per session. 16women in the control group didn't participate in the exercise program. The independent variable was a exercise program for prevention of falling. Dependent variables were physical fitness, posture and fall prevention self-efficacy. Prevention of falling exercise program is consisted of an elastic band using exercise and Korean dance movement exercise. Physical fitness consisted of grip strength, upper and lower body endurance, cardiovascular endurance, flexibility, balance, coordination. The posture was measured the static posture when standing, using a high-resolution camera, body style to automatically measure the distance and angle(M-zen, Korea). Posture was measured in both the coronal and sagittal plane via reference board. Fall prevention self-efficacy was measured via questionnaire using the Korea Falls Self-Efficacy Scale (FES-K). The physical fitness, posture and fall prevention self-efficacy were measured twice with pre and post exercise, and the difference between groups with Wilcox signed rank test, and the group-specific post verification was carried out with U-validated methods (Mann Whitney U test). Statistical significance level was verified by setting the p<.05. Lower body endurance, cardiovascular endurance, flexibility, balance and coordination significantly increased in the experimental group. The control group was no significant increase in physical fitness variables. shoulder slope angle, pelvic slope angle(coronal/sagittal), leg length difference, scapular inferior angle and left/right calcaneus angle significantly decreased in the experimental group. Both the experimental group and control group were no significant increase in fall prevention self efficacy. The prevention of falling exercise program for elderly women indicated the positive changes in physical fitness(except grip strength) and posture(except upper body slope). However, there are no significant differences of falling prevention self-efficacy between the both group. Thus, the prevention of falling exercise program for the elderly has been proved that it is highly efficient on improving physical fitness and posture proofreading. However, we still need to consider supplement exercise for grip strength and upper body slope.

• Journal of the Korean Orthopaedic Association
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• v.53 no.6
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• pp.498-504
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• 2018

Purpose: To evaluate the clinical and radiological outcomes, and the complications of unicompartmental knee arthroplasty (UKA) using a fixed bearing prosthesis after 5-year follow-up. Materials and Methods: Twenty-six knees (25 patients) that underwent fixed bearing UKA between May 2003 and August 2011 were included. The subjects were 3 males (3 knees) and 22 females (23 knees), and the average age was 63.5 years. The preoperative diagnosis was osteoarthritis (23 knees) and osteonecrosis (3 knees). The mean follow-up duration was 67 months (from 60 to 149 months). The clinical evaluation included pre- and postoperative American knee society knee and function score, and range of motion. The radiology evaluation included standing antero-posterior, lateral view, and fluoroscopic film to analyze the postoperative alignment and osteolysis. Results: The mean American Knee Society knee score and function score were improved from 42.0 and 57.5 to 87.9 and 85.0, respectively (p<0.001). The mean preoperative and postoperative range of motion was $132.9^{\circ}$ and $132.5^{\circ}$, respectively. The mean femorotibial angle were varus $0.5^{\circ}$ preoperatively and valgus $2.2^{\circ}$ postoperatively. A radiolucent line was observed in 2 knees; one knee had a stable implant, while in the other knee, patellofemoral arthritis was identified during UKA. Diffuse pain of the knee joint with tenderness of the medial joint line was identified at the follow-up, so conversion to total knee arthroplasty was recommended. No other complications, such as osteolysis, infections, postoperative stiffness, and dislocation, were encountered. Conclusion: The midterm results of fixed bearing UKA were clinically and radiologically satisfactory.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.