• Journal of Life Science
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• v.22 no.2
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• pp.184-191
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• 2012

The purpose of this study was to examine the effect of 16 weeks of resistance training on the fatigue factor, muscle soreness, oxidative stress, and myokine in elite weightlifters. A total of 10 subjects (six male, four female) participated in this study. The results were compared according to baseline, 8 weeks, and 16 weeks. Ammonia and Pi were increased through 16 weeks of resistance training, but this result was not significant. CK was significantly (p<0.05) increased at 8 weeks and 16 weeks compared to baseline, while LDH was significantly (p<0.05) increased at 8 weeks compared to baseline. The MDA of the oxidative stress factor was significantly (p<0.05) increased at 8 weeks compared to baseline and 16 weeks, and TAS of the antioxidant factor was significantly (p<0.05) increased at 8 weeks compared to baseline. The IL-15 of the myokine was significantly (p<0.05) increased at baseline compared to 8 weeks and 16 weeks. In conclusion, 16 weeks of high-intensity resistance training may have a positive effect on peripheral fatigue factors, muscle soreness, oxidative stress, and myokine in elite weightlifters.

• The KIPS Transactions:PartB
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• v.14B no.4
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• pp.287-294
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• 2007

Many classification algorithms for real world data suffer from a data class imbalance problem. To solve this problem, various methods have been proposed such as altering the training balance and designing better sampling strategies. The previous methods are not satisfy in the distribution of the input data and the constraint. In this paper, we propose a focused sampling method which is more superior than previous methods. To solve the problem, we must select some useful data set from all training sets. To get useful data set, the proposed method devide the region according to scores which are computed based on the distribution of SOM over the input data. The scores are sorted in ascending order. They represent the distribution or the input data, which may in turn represent the characteristics or the whole data. A new training dataset is obtained by eliminating unuseful data which are located in the region between an upper bound and a lower bound. The proposed method gives a better or at least similar performance compare to classification accuracy of previous approaches. Besides, it also gives several benefits : ratio reduction of class imbalance; size reduction of training sets; prevention of over-fitting. The proposed method has been tested with kNN classifier. An experimental result in ecoli data set shows that this method achieves the precision up to 2.27 times than the other methods.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.5
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• pp.323-330
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• 2023

The development of an analysis model that reflects the microstructure characteristics of polyvinyl alcohol (PVA) fiber-reinforced cementitious composites, which have a highly complex microstructure, enables synergy between efficient material design and real experiments. PVA fiber orientations are an important factor that influences the mechanical behavior of PVA fiber-reinforced cementitious composites. Owing to the difficulty in distinguishing the gray level value obtained from micro-CT images of PVA fibers from adjacent phases, fiber segmentation is time-consuming work. In this study, a micro-CT test with a voxel size of 0.65 ㎛³ was performed to investigate the three-dimensional distribution of fibers. To segment the fibers and generate training data, histogram, morphology, and gradient-based phase-segmentation methods were used. A U-net model was proposed to segment fibers from micro-CT images of PVA fiber-reinforced cementitious composites. Data augmentation was applied to increase the accuracy of the training, using a total of 1024 images as training data. The performance of the model was evaluated using accuracy, precision, recall, and F1 score. The trained model achieved a high fiber segmentation performance and efficiency, and the approach can be applied to other specimens as well.

• Journal of the Korean Society of Physical Medicine
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• v.11 no.1
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• pp.127-132
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• 2016

PURPOSE: The purpose of this study was to investigate the effects of stage-based training, including core exercises, on scoliosis patients. METHODS: Two patients with scoliosis participated in the study. Both patients participated for eight months and were trained for an hour three times a week. The training program consisted of stretching and strengthening, as well as core exercises, and was divided into five stages. The Cobb angles and trunk lengths of the subjects were measured after one month, two months, and four months of training. Measurements were also taken after the subjects completed training. All of the measurements were taken using Formetric 4D. RESULTS: The Cobb's angle of subject A, which was $41^{\circ}$ before training, measured $30^{\circ}$ following training. The Cobb's angle of subject B also improved from $41^{\circ}$ prior to training to $34^{\circ}$ after training. Furthermore, the trunk lengths of both subjects improved. The trunk length of subject A increased from 438 mm to 450 mm and, and the trunk length of subject B increased from 433 mm to 458 mm. CONCLUSION: This study has shown that stage-based training and core training can be used as effective treatments for scoliosis patients.

• Journal of the Korean Society of Physical Medicine
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• v.15 no.4
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• pp.145-153
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• 2020

PURPOSE: This study compares the effects of HUBER rehabilitation and general rehabilitation treatment on the coordination, balance, and walking ability of stroke patients. METHODS: This study enrolled 38 randomized stroke patients, and data was collected for 6 weeks. All participants were randomly assigned to either the experimental group (n = 19) or control group (n = 19). The experimental group were administered Huber rehabilitation and general rehabilitation treatment. The control group was given only general rehabilitation treatment. Both treatments were conducted for 30 minutes during each training session, 3 training sessions per week, for 6 weeks. The coordination, balance, and walking ability were evaluated before and after the intervention, to compare the intergroup and intragroup changes. RESULTS: Change in the right LOS (limit of stability) (p < .001) and forward LOS (p < .02) following intervention were significantly greater in the experimental group than in the control group, but no significant group difference was observed between left LOS (p > .1) and backward LOS (p > .2). Alterations in coordination (p < .02) and TUG (p <. 05) were significantly greater after intervention in the experimental group than in the control group. CONCLUSION: These findings suggest that HUBER rehabilitation is effective in improving the coordination, balance, and walking ability in stroke patients. To strengthen and validate the results of this study, future studies related to HUBER rehabilitation are required.

• Journal of Korea Game Society
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• v.18 no.1
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• pp.19-26
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• 2018

In this study, the hardcore game user verified the manipulation delay that occurred during VR game play because of the experienced game. Based on the HCI - based research approach, we created a 2D, 3D, and VR prototype game with user manipulation cycle hypothesis. Based on this, 121 users were experimented with 2D, 3D, VR format user interface. The average user manipulation period extracted by the experiment was compared with the independent sample T test. Based on the test results give the average time difference between the user's operation of the 2D VR format has been verified. User operation period of the average time difference in 3D VR format proved the null hypothesis of no significant difference has been adopted.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.1
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• pp.74-81
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• 2003

In this study, we'd implemented a real time embedded speech recognition system that requires minimum memory size for speech recognition engine and DB. The word to be recognized consist of 40 commands used in a PCS phone and 10 digits. The speech data spoken by 15 male and 15 female speakers was recorded and analyzed by short time analysis method, which window size is 256. The LPC parameters of each frame were computed through Levinson-Burbin algorithm and they were transformed to Cepstrum parameters. Before the analysis, speech data should be processed by pre-emphasis that will remove the DC component in speech and emphasize high frequency band. Baum-Welch reestimation algorithm was used for the training of HMM. In test phone, we could get a recognition rate using likelihood method. We implemented an embedded system by porting the speech recognition engine on ARM core evaluation board. The overall recognition rate of this system was 95%, while the rate on 40 commands was 96% and that 10 digits was 94%.

• Composites Research
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• v.32 no.5
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• pp.279-283
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• 2019

A flight training simulator of a fully spherical configuration is being developed to precisely and quickly control six degrees of freedom (Dof) motions especially with unlimited rotations. The full-scale simulator should be designed with a lightweight material to reduce inertial effects for fast and stable feedback controls while no structural failure is ensured during operations. In this study, a sandwich composite consisting of glass fiber reinforced plastics and a foam core is used to obtain high specific strengths and specific stiffnesses. T-type stainless steel frames are inserted to minimize the deformation of the sphere curvature. Finite element analysis is carried out to evaluate structural safety of the simulator composed of the sandwich sphere and steel frames. The analysis considers the weights of the equipment and trainee and it is assumed to be 200 kg. Gravity acceleration is also considered. The stresses and displacement acting on the simulator are calculated and the safety is assessed under two different situations.

• Korean Security Journal
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• no.56
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• pp.83-105
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• 2018

Special security guards play the role to safely operate and manage nuclear power plants from unintended internal and external threats. Physical fitness management of special security guards is one of the most important factors for improving and maintaining the level of physical protection. Thus, the purpose of this study is to analyze the physical fitness factor and physical fitness level required for mission accomplishment through job analysis of special security guards. The special security guards of Nuclear Power Plant in Korea were performing 7 jobs, 26 duties, 159 tasks. In order to accomplish theses tasks, the following physical fitness were required: muscle strength and muscle endurance of the hand, upper limb, lower limb and core, quickness, agility and Cardio function. The duties that require a lot of physical fitness were in the order of conducting arrest and self-defense, conducting unarmed defensive tactics, demonstrating proficiency with semiautomatic rifle, using protective equipment, performing emergency plan and defensive strategy, etc. The results of this study are expected to provide basic data necessary for establishing guidelines for fitness qualification and training of special security guards in the future and contribute to enhancement of physical protection of nuclear power plants.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.35-45
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• 2022

Even in recent years, treatment of first-aid patients is still often delayed due to a shortage of medical resources in marginalized areas. Research on automating the analysis of medical data to solve the problems of inaccessibility for medical services and shortage of medical personnel is ongoing. Computer vision-based medical inspection automation requires a lot of cost in data collection and labeling for training purposes. These problems stand out in the works of classifying lesion that are rare, or pathological features and pathogenesis that are difficult to clearly define visually. Anomaly detection is attracting as a method that can significantly reduce the cost of data collection by adopting an unsupervised learning strategy. In this paper, we propose methods for detecting abnormal images on chest X-RAY images as follows based on existing anomaly detection techniques. (1) Normalize the brightness range of medical images resampled as optimal resolution. (2) Some feature vectors with high representative power are selected in set of patch features extracted as intermediate-level from lesion-free images. (3) Measure the difference from the feature vectors of lesion-free data selected based on the nearest neighbor search algorithm. The proposed system can simultaneously perform anomaly classification and localization for each image. In this paper, the anomaly detection performance of the proposed system for chest X-RAY images of PA projection is measured and presented by detailed conditions. We demonstrate effect of anomaly detection for medical images by showing 0.705 classification AUROC for random subset extracted from the PadChest dataset. The proposed system can be usefully used to improve the clinical diagnosis workflow of medical institutions, and can effectively support early diagnosis in medically poor area.