• Korean Journal of Applied Biomechanics
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• v.16 no.4
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• pp.73-81
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• 2006

The purpose of this study was to investigate the effects of strength training on knee joint torque during walking in an adolescent with trisomy-21 Down syndrome. One adolescent with Down syndrome and one normal child participated in this study. Strength training consisted of eight exercises: squat, hamstring curl, hip adduction, hip abduction, knee extension, toe raise, sit-ups, and hyperextension of the waist. The participant with Down syndrome was participated in strength training for 12 weeks, three times a week, three sets, 10-15 RM; resistance was adjusted according to the principle of progressive overload. To measure the effect of strength training, isokinetic strength variables and knee joint torques were measured before training and after 12 weeks of training. The participant with Down syndrome had some abnormalities in controlling knee motion during walking due to muscle hypotonia, ligament laxity, and weakness of muscles. Post-training isokinetic strength increased compared to pre-training measurements. Knee range of motion were increased after strength training. Strength training did not affect ad/adduction and in/exteranl moments but did have an effect on flexor/extensor moment and timing.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.262-273
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• 2024

We provide a detailed analysis of the data processing and model training process for vulnerability classification using Transformer-based language models, especially sentence text-to-text transformers (ST5)-XXL and XLNet. The main purpose of this study is to compare the performance of the two models, identify the strengths and weaknesses of each, and determine the optimal learning rate to increase the efficiency and stability of model training. We performed data preprocessing, constructed and trained models, and evaluated performance based on data sets with various characteristics. We confirmed that the XLNet model showed excellent performance at learning rates of 1e-05 and 1e-04 and had a significantly lower loss value than the ST5-XXL model. This indicates that XLNet is more efficient for learning. Additionally, we confirmed in our study that learning rate has a significant impact on model performance. The results of the study highlight the usefulness of ST5-XXL and XLNet models in the task of classifying security vulnerabilities and highlight the importance of setting an appropriate learning rate. Future research should include more comprehensive analyzes using diverse data sets and additional models.

• The Journal of Korean Physical Therapy
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• v.12 no.1
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• pp.9-13
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• 2000

The purpose of this study was to determine the overflow effect and cross training effect of isometric quadriceps training that performed in specific angle of unilateral let. Ten healthy students with an average age of 24 years$(24.1\pm1.3)$, were participated in this study. Then 5 subjects in each group were chosen at random to train using only right quadriceps muscle two time per day(group 2), five times a week and the other 5 subjects(group 1) were chosen to train one times per day, five times a week for 2 weeks at only 50 degrees (contract 6 seconds, rest 10 seconds, 3 sets). Before and after the training, isometric quadriceps muscle testing of the both leg was Performed at three different angles, 60, 50 and 40 degrees respectively by BHN-COM (isokinetic dynamometer) in sitting position. The data was analyzed with paired t-test to determine significant difference between before and after training. In this study, we have found that the isometric quadriceps muscle training on specific angle of right side produced overflow effect In healthy subjects. However, increasing the peak torque of specific angle(training angle) of trained limb did not have an effect on increasing the peak torque of contralateral limb. These results demonstrate that the cross training effect did nut found in this study but a alight increase of peak torque of the untrained limb would recognized the possibility of cross training effect.

• IE interfaces
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• v.16 no.spc
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• pp.7-13
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• 2003

This paper is concerned with a six sigma green belt training program. Comparative studies of existing training programs for three major companies (Samsung Electronics Company, Hyundai Motor Company, and LG Chemical Ltd.) and two consulting firms (Korean Standards Association and Korea Management Association) are made. Based on the comparative studies, a new green belt training program is proposed. The main focus of this program is on manufacturing, specially on cost and waste reductions, yield improvement, and operations with opportunity to improve capacity without major capital expenditure. The green belts take up to 4 or 5 days of intensive, highly quantitative training, roughly corresponding to the five macro phases of the six sigma methodology: define, measure, analyze, improve, and control. The six sigma tool sets for each phase are also specified.

• Journal of the Korean Applied Science and Technology
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• v.37 no.1
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• pp.114-123
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• 2020

The present study aimed the effects of power resistance training(PRT) on fitness, muscle mass and short physical performance battery(SPPB) elderly women. Thirty older woman(aged 70 over years) were divided in two groups : PRT(n=15) and traditional resistance training(TRT)(n=15). The volunteers trained three times a week, during 12weeks. Both groups performed an equal work output with load red color thera-band. Three sets of twelve repetitions of each exercise were performed with rest intervals of 60s between sets. According to the results. Main fitness were significantly difference in grip strength(TRT) and cardiopulmonary. Muscle was not significantly difference. And Timed up and go and 400m walking(TRT) were significantly difference. There is sufficient effects on between training method in fitness, SPPB but PRT training may yield better results compared with TRT.

• PNF and Movement
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• v.18 no.1
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• pp.87-95
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• 2020

Purpose: The purpose of this study was to investigate the effect of whole-body vibration training on the flexibility and agility of professional soccer players. Methods: Sixteen professional soccer players participated voluntarily in the study. Subjects were allocated to two groups: the experimental group received whole-body vibration (WBV) and team training, and the control group received only team training. Team training was conducted in 15 sessions of 70 min duration over 3 weeks. WBV training was applied at 40 Hz frequency, 5 sets (1 min-training, 1 min-resting) in a squatting position. Outcomes from sit-and-reach, side-step test, burpee test, and T-test were measured before and after training. To examine pre- and post-intervention differences between the two groups, a paired t-test was used. Independent t-tests were performed to compare pre- and post-test scores and the time difference of the two groups. Results: Significant improvements in sit-and-reach and agility variables were observed in the experimental group (p < 0.05). In particular, flexibility (sit-and-reach) and agility (the side-step test and the burpee test) were significantly different between the two groups (p < 0.05). Conclusion: These findings suggest that whole-body vibration training has a positive effect on performance enhancement for professional soccer players.

• Computers and Concrete
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• v.34 no.2
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• pp.151-168
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• 2024

This research aimed to appraise the effectiveness of four optimization approaches - cuckoo optimization algorithm (COA), multi-verse optimization (MVO), particle swarm optimization (PSO), and teaching-learning-based optimization (TLBO) - that were enhanced with an artificial neural network (ANN) in predicting the bearing capacity of shallow foundations located on cohesionless soils. The study utilized a database of 97 laboratory experiments, with 68 experiments for training data sets and 29 for testing data sets. The ANN algorithms were optimized by adjusting various variables, such as population size and number of neurons in each hidden layer, through trial-and-error techniques. Input parameters used for analysis included width, depth, geometry, unit weight, and angle of shearing resistance. After performing sensitivity analysis, it was determined that the optimized architecture for the ANN structure was 5×5×1. The study found that all four models demonstrated exceptional prediction performance: COA-MLP, MVO-MLP, PSO-MLP, and TLBO-MLP. It is worth noting that the MVO-MLP model exhibited superior accuracy in generating network outputs for predicting measured values compared to the other models. The training data sets showed R² and RMSE values of (0.07184 and 0.9819), (0.04536 and 0.9928), (0.09194 and 0.9702), and (0.04714 and 0.9923) for COA-MLP, MVO-MLP, PSO-MLP, and TLBO-MLP methods respectively. Similarly, the testing data sets produced R² and RMSE values of (0.08126 and 0.07218), (0.07218 and 0.9814), (0.10827 and 0.95764), and (0.09886 and 0.96481) for COA-MLP, MVO-MLP, PSO-MLP, and TLBO-MLP methods respectively.

• Journal of KIISE:Software and Applications
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• v.31 no.10
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• pp.1259-1265
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• 2004

In many applications, producing labeled data is costly and time consuming while an enormous amount of unlabeled data is available with little cost. Therefore, it is natural to ask whether we can take advantage of these unlabeled data in classification teaming. In machine learning literature, the co-training method has been widely used for this purpose. However, the current co-training method requires the entire features to be split into two independent sets. Therefore, in this paper, we improved the current co-training method in a number of ways, and proposed a new co-training method which do not need the feature split. Experimental results show that our proposed method can significantly improve the performance of the current co-training algorithm.

• MALSORI
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• no.62
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• pp.97-112
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• 2007

The speech recognizer in general operates in noisy acoustical environments. Many research works have been done to cope with the acoustical variations. Among them, the multiple-HMM model approach seems to be quite effective compared with the conventional methods. In this paper, we consider a multiple-model approach combined with the model compensation method and investigate the necessary number of the HMM model sets through noisy speech recognition experiments. By using the data-driven Jacobian adaptation for the model compensation, the multiple-model approach with only a few model sets for each noise type could achieve comparable results with the re-training method.

• International Journal of Contents
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• v.8 no.2
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• pp.7-12
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• 2012

Imbalanced data sets are difficult to be classified since most classifiers are developed based on the assumption that class distributions are well-balanced. In order to improve the error back-propagation algorithm for the classification of imbalanced data sets, a new error function is proposed. The error function controls weight-updating with regards to the classes in which the training samples are. This has the effect that samples in the minority class have a greater chance to be classified but samples in the majority class have a less chance to be classified. The proposed method is compared with the two-phase, threshold-moving, and target node methods through simulations in a mammography data set and the proposed method attains the best results.