• Journal of the Korean Society of Physical Medicine
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• v.14 no.3
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• pp.63-71
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• 2019

PURPOSE: This study was examined the effects of coordinative locomotor training (CLT) on the postural imbalance and gait in children. METHODS: Four children were sampled as subjects. A single subject study (A-B-A') was conducted by measuring the following: baseline five sessions;, intervention phase, 15 sessions;, and postline (A') five sessions. The research period was eight weeks. The CLT program consisted of warming-up exercise, main exercise, and finishing exercise, and it was performed for one hour per day. A oneleg standing test (OLST) was performed determine the static balance. A functional reach test (FRT) was performed determine the reactionary balance. To determine the dynamic balance, the time up and go test (TUG) was performed. A 10m walking test (10 MWT) was performed to determine the walking ability. A statistical test was performed through descriptive statistics to present the average and standard deviation, and the variation rate was compared using a visual analysis method with graphs. RESULTS: As a result of CLT application, all four subjects improved the OLST, FRT, TUG, and 10 MWT compared to the intervention period baseline, and postline period. CONCLUSION: CLT appeared to improve the posture imbalance and gait in children.

• PNF and Movement
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• v.17 no.3
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• pp.411-419
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• 2019

Purpose: This study determined the effects of elastic bands in coordinative locomotor training on the body alignment of elementary school baseball players. Methods: Thirty subjects were recruited for this study and separated into two groups: the coordinative locomotor training group with elastic bands (n=15) and the non-training control group (n=15) were five times a week for eight. The trunk inclination, trunk imbalance, kyphotic angle and lordotic angle were used to evaluate body alignment. Results: The experimental group showed significant improvements in trunk inclination, trunk imbalance, kyphotic angle and lordotic angle (p<0.05). Conclusion: Coordinative locomotor training impacted postural alignment in elementary school baseball players.

• Geophysics and Geophysical Exploration
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• v.23 no.3
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• pp.131-140
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• 2020

In training a classification model using machine learning, the acquisition of training data is a very important stage, because the amount and quality of the training data greatly influence the model performance. However, when the cost of obtaining data is so high that it is difficult to build ideal training data, the number of samples for each class may be acquired very differently, and a serious data-imbalance problem can occur. If such a problem occurs in the training data, all classes are not trained equally, and classes containing relatively few data will have significantly lower recall values. Additionally, the reliability of evaluation indices such as accuracy and precision will be reduced. Therefore, this study sought to overcome the problem of data imbalance in two stages. First, we introduced weighted accuracy and weighted precision as new evaluation indices that can take into account a data-imbalance ratio by modifying conventional measures of accuracy and precision. Next, oversampling was performed to balance weighted precision and recall among classes. We verified the algorithm by applying it to the problem of facies classification. As a result, the imbalance between majority and minority classes was greatly mitigated, and the boundaries between classes could be more clearly identified.

• Smart Structures and Systems
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• v.29 no.1
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• pp.63-75
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• 2022

The raw data collected by structural health monitoring (SHM) systems may suffer multiple patterns of anomalies, which pose a significant barrier for an automatic and accurate structural condition assessment. Therefore, the detection and classification of these anomalies is an essential pre-processing step for SHM systems. However, the heterogeneous data patterns, scarce anomalous samples and severe class imbalance make data anomaly detection difficult. In this regard, this study proposes a convolutional neural network-based data anomaly detection method. The time and frequency domains data are transferred as images and used as the input of the neural network for training. ResNet18 is adopted as the feature extractor to avoid training with massive labelled data. In addition, the focal loss function is adopted to soften the class imbalance-induced classification bias. The effectiveness of the proposed method is validated using acceleration data collected in a long-span cable-stayed bridge. The proposed approach detects and classifies data anomalies with high accuracy.

• PNF and Movement
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• v.7 no.4
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• pp.37-43
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• 2009

Purpose : The purpose of this study was to evaluate the effect of core stability training at deep abdominal muscle for balance control of hemiplegic patient. Method : The subject of this study was a 47-year-old man with right hemiplegia. He was treated five times a week for three weeks with core stability training at deep abdominal muscles. Evaluation tool was used Functional reach test(FRT), timed up and go test(TUG) and one leg standing for stroke patients. Result : The FRT distance increase, TUG time decrease, one leg standing time increase core stability training at deep abdominal muscles for right hemiplegia improved was the ability for maintain balance. Posture and control of trunk stability are changing posture, and so which showed significant improve of total balance control. Conclusion : The result of this study showed that core stability training at deep abdominal muscles is an effective treatment for balance control. Therefore, it could be considered as a treatment method in the rehabilitation of stroke patient with poor postural control and imbalance, although further studies are needed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.9C
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• pp.905-912
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• 2007

This paper proposes an efficient estimation and compensation scheme of IQ imbalance for OFDM-based WLAN systems in the presence of symbol timing error. Since the conventional scheme assumes perfect time synchronization, the criterion of the scheme used to derive the estimation of IQ imbalance is inadequate in the presence of the symbol timing error and the system performance is seriously degraded. New criterion and compensation scheme considering the effect of symbol timing error are proposed. With the proposed scheme, the IQ imbalance can be almost perfectly eliminated in the presence of symbol timing error. The bit error rate performance of the proposed scheme is evaluated by the simulation. In case of 54 Mbps transmission mode in IEEE 802.11a system, the proposed scheme achieves a SNR gain of 4.3dB at $BER=2{\cdot}10^{-3}$. The proposed compensation algorithm of IQ imbalance is implemented using Verilog HDL and verified. The proposed IQ imbalance compensator is composed of 74K logic gates and 6K bits memory from the synthesis result using 0.18um CMOS technology.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.1
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• pp.431-440
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• 2017

The purpose of this study was to investigate the effects of Iyengar yoga practice on the lower body imbalance in middle-aged women. The subjects (n=24), who had not performed yoga training prior to this study (and) were not attending any other training programs, participated after undergoing an X-RAY examination with the Gonstead Technique and then their lower body imbalance (was reevaluated). The subjects completed the yoga program for 12 weeks (3 times per week, 90 minutes per session). The data were analyzed with the paired t-test and alpha was set at 0.05. It was found that 1) the height differences between the right and left iliac crests (p < 0.001), width (p < 0.001) and length (p < 0.001) differences between the right and left iliac fossa, and width differences between the right and left sacrum (p < 0.001) were significantly reduced after the training program. In addition, 2) the lower limb length discrepancy was significantly reduced (p < 0.001). Our data suggest that Iyengar yoga training for 12 weeks reduces the pelvic imbalance and length differences between the right and left lower limbs in middle-aged females.

• The Journal of the Acoustical Society of Korea
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• v.41 no.6
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• pp.647-654
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• 2022

Although the fields in which is utilized children's speech recognition is on the rise, the lack of quality data is an obstacle to improving children's speech recognition performance. This paper proposes a new method for improving children's speech recognition performance by additionally using adult speech data. The proposed method is a transformer based domain adversarial training using dynamically weighted loss to effectively address the data imbalance gap between age that grows as the amount of adult training data increases. Specifically, the degree of class imbalance in the mini-batch during training was quantified, and the loss function was defined and used so that the smaller the data, the greater the weight. Experiments validate the utility of proposed domain adversarial training following asymmetry between adults and children training data. Experiments show that the proposed method has higher children's speech recognition performance than traditional domain adversarial training method under all conditions in which asymmetry between age occurs in the training data.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.251-256
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• 2022

In this paper, we propose the effects of coordinative locomotor training in body alignment of high school baseball players. Coordinative locomotor training was applied to 20 subjects in the experimental group for 30 minutes, 5timess aweek for 4 weeks. Body alignmen was measured using a formertic, and variables representing body alignment included trunk inclination, trunk imbalance, pelvic tilt, pelvic torson, kyphotic angle and lordotic angle. The results of this study were as follows: As for the Body alignment, there were significantly increased in kyphotic angle and lordotic angle in the experimental group. From the above results, it seems that coordinative locomotor training has a positive effects on the body alignment of high school baseball players. The coordinative locomotor training was able to produce confirmation that body alignment change in the case of effective exercise interventions in high school baseball players. Coordinative locomotor training is thought to be effective in preventing physical imbalance in high school baseball.

• Journal of Information Technology Services
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• v.11 no.4
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• pp.233-250
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• 2012

In this paper, credit card delinquency means the possibility of occurring bad debt within the certain near future from the normal accounts that have no debt and the problem is to predict, on the monthly basis, the occurrence of delinquency 3 months in advance. This prediction is typical binary classification problem but suffers from the issue of data imbalance that means the instances of target class is very few. For the effective prediction of bad debt occurrence, Support Vector Machine (SVM) with kernel trick is adopted using credit card usage and payment patterns as its inputs. SVM is widely accepted in the data mining society because of its prediction accuracy and no fear of overfitting. However, it is known that SVM has the limitation in its ability to processing the large-scale data. To resolve the difficulties in applying SVM to bad debt occurrence prediction, two stage clustering is suggested as an effective data reduction method and ensembles of SVM models are also adopted to mitigate the difficulty due to data imbalance intrinsic to the target problem of this paper. In the experiments with the real world data from one of the major domestic credit card companies, the suggested approach reveals the superior prediction accuracy to the traditional data mining approaches that use neural networks, decision trees or logistics regressions. SVM ensemble model learned from T2 training set shows the best prediction results among the alternatives considered and it is noteworthy that the performance of neural networks with T2 is better than that of SVM with T1. These results prove that the suggested approach is very effective for both SVM training and the classification problem of data imbalance.