• Physical Therapy Korea
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• v.30 no.2
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• pp.128-135
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• 2023

Background: Despite the fact that aquatic exercise is one of the most popular alternative treatment methods for children with cerebral palsy (CP), there are few research regarding its effectiveness. Objects: The purpose of this study was to examine the effects of aquatic exercise on upper extremity function and postural control during reaching in children with CP. Methods: Ten participants (eight males and two females; 4-10 years; Gross Motor Function Classification System levels II-IV) with spastic diplegia were recruited to this study. The aquatic exercise program consisted of four modified movements that were selected from the Halliwick 10-point program to enhance upper extremity and trunk movements. The participants attended treatment two times a week for 6 weeks, averaging 35 minutes each session. The Box and Block Test (BBT), transferring pennies in the Bruininks-Oseretsky Test (BOT), and pediatric reaching test (PRT) scores were used as clinical measures. Three-dimensional motion analysis system was used to collect and analyze kinematic data. Differences in BBT and BOT values among pre-treatment, post-treatment, and retention (after 3 weeks) were analyzed using a Friedman test. In addition, the PRT scores and variables (movement time, hand velocity, straightness ratio, and number of movement units) from the three-dimensional motion analysis were tested using a Wilcoxon signed-rank test. The significance level was established at p < 0.05. When the results appeared to be statistically significant, a post-hoc test for multiple comparisons was performed with the Wilcoxon signed-rank test. Results: All clinical measures, which included BBT, transferring pennies of BOT, and PRT, were significantly increased between pre-intervention and post-intervention scores and between pre-intervention and retention scores after treatment (p = 0.001). Three-dimensional motion analysis mostly were significantly improved after treatment (p = 0.001). Conclusion: Aquatic exercise may help to improve body function, activity, and participation in children with varying types of physical disabilities.

• Resources Recycling
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• v.26 no.2
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• pp.46-55
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• 2017

In this study, the design methodology of Radial Basis Function Neural Networks is developed with the aid of Laser Induced Breakdown Spectroscopy and also applied to the practical plastics sorting system. To identify black plastics such as ABS, PP, and PS, RBFNNs classifier as a kind of intelligent algorithms is designed. The dimensionality of the obtained input variables are reduced by using PCA and divided into several groups by using K-means clustering which is a kind of clustering techniques. The entire data is split into training data and test data according to the ratio of 4:1. The 5-fold cross validation method is used to evaluate the performance as well as reliability of the proposed classifier. In case of input variables and clusters equal to 5 respectively, the classification performance of the proposed classifier is obtained as 96.78%. Also, the proposed classifier showed superiority in the viewpoint of classification performance where compared to other classifiers.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.431-440
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• 2019

This study was conducted to investigate changes in activities of daily living (ADLs) according to the Gross Motor Function Classification System (GMFCS) after one year of physical and occupational therapy and to compare the responsiveness of ADL tools. A total of 48 children with spastic cerebral palsy participated in the study. The GMFCS, Functional Independence Measure for Children (WeeFIM), and Pediatric Evaluation of Disability Inventory (PEDI) were measured. The results showed that the GMFCS was significantly correlated with the PEDI (p<0.05), while there was a significant difference in the change of ADLs measured by the PEDI, but not the WeeFIM. There was a significant difference in the changes in ADLs according to the GMFCS, and the change in ADLs in the high functional level group was significantly higher than in the low functional level group (p<0.05). After physical and occupational therapy, the degree of improvement of ADLs varied according to the GMFCS, but seemed to be improved in a clinically meaningfully way. The PEDI is sensitive to changes in ADLs, so it may be used widely in clinical practice.

• The Journal of Korean Physical Therapy
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• v.26 no.3
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• pp.209-215
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• 2014

Purpose: The purpose of this study is to examine the effects of postural control training using a horseback riding simulation on the spinal alignment of children with cerebral palsy. Methods: This study was conducted with 30 children with cerebral palsy at levels I~IV in the Gross Motor Function Classification System (GMFCS), and they were randomly divided into a control group and a hippotherapy group. Both the control group and the experimental group received NDT for 30 minutes per session, four times per week for ten weeks, while the experimental group also received hippotherapy 15 minutes per session, four times per week for ten weeks, after the neurodevelopmental treatment (NDT). The horseback riding simulators (JOBA, EU7805, Panasonic) used in this study simulated actual horse movements. Trunk imbalance, pelvic torsion, and pelvic tilt were measured in each group before the exercise and five weeks and ten weeks after the beginning of the exercise using a spinal structure analysis system (ABW Mapper). Results: The Intra-group effects on trunk imbalance, pelvic torsion, and pelvic tilt according to the exercise periods after the hippotherapy were tested, and the results showed significant interaction effects between the groups and the periods (p<0.05). Conclusion: The horseback riding simulation exercise was shown to be effective for the spinal alignment of children with cerebral palsy. Therefore, additional studies should be conducted with more children with CP divided by type.

• The Transactions of the Korea Information Processing Society
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• v.4 no.9
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• pp.2299-2308
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• 1997

While most machine learning research has been primarily concerned with the development of systems that implement one type of learning strategy, we use a multistrategy approach which integrates rule induction learning and instance-based learning, and show how this marriage allows for overall better performance. In the rule induction learning phase, we derive an entropy function, based on Hellinger divergence, which can measure the amount of information each inductive rule contains, and show how well the Hellinger divergence measures the importance of each rule. We also propose some heuristics to reduce the computational complexity by analyzing the characteristics of the Hellinger measure. In the instance-based learning phase, we improve the current instance-based learning method in a number of ways. The system has been implemented and tested on a number of well-known machine learning data sets. The performance of the system has been compared with that of other classification learning technique.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3806-3825
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• 2016

This paper brings out a neoteric frame of reference for visual semantic based 3d video search and retrieval applications. Newfangled 3D retrieval application spotlight on shape analysis like object matching, classification and retrieval not only sticking up entirely with video retrieval. In this ambit, we delve into 3D-CBVR (Content Based Video Retrieval) concept for the first time. For this purpose we intent to hitch on BOVW and Mapreduce in 3D framework. Here, we tried to coalesce shape, color and texture for feature extraction. For this purpose, we have used combination of geometric & topological features for shape and 3D co-occurrence matrix for color and texture. After thriving extraction of local descriptors, TB-PCT (Threshold Based- Predictive Clustering Tree) algorithm is used to generate visual codebook. Further, matching is performed using soft weighting scheme with L₂ distance function. As a final step, retrieved results are ranked according to the Index value and produce results .In order to handle prodigious amount of data and Efficacious retrieval, we have incorporated HDFS in our Intellection. Using 3D video dataset, we fiture the performance of our proposed system which can pan out that the proposed work gives meticulous result and also reduce the time intricacy.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.5
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• pp.423-430
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• 2017

Recently, data analysis research has been carried out using the deep learning technique in various fields such as image interpretation and/or classification. Various types of algorithms are being developed for many applications. In this paper, we propose a precipitation prediction algorithm based on deep learning with high accuracy in order to take care of the possible severe damage caused by climate change. Since the geographical and seasonal characteristics of Korea are clearly distinct, the meteorological factors have repetitive patterns in a time series. Since the LSTM (Long Short-Term Memory) is a powerful algorithm for consecutive data, it was used to predict precipitation in this study. For the numerical test, we calculated the PWV (Precipitable Water Vapor) based on the tropospheric delay of the GNSS (Global Navigation Satellite System) signals, and then applied the deep learning technique to the precipitation prediction. The GNSS data was processed by scientific software with the troposphere model of Saastamoinen and the Niell mapping function. The RMSE (Root Mean Squared Error) of the precipitation prediction based on LSTM performs better than that of ANN (Artificial Neural Network). By adding GNSS-based PWV as a feature, the over-fitting that is a latent problem of deep learning was prevented considerably as discussed in this study.

• Proceedings of the Korean Society of Applied Pharmacology
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• 1997.11a
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• pp.57-74
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• 1997

To make a proposal for the revision of KGCP, ICH Harmonized Tripartite Guideline for Good Clinical Practice, which is on the stage of worldwide implementation, was compared with current GCPs of tripartite countries of ICH, namely USA, Europe and Japan as well as Korea. On the basis of the classification in ICH GCP, comprehensive comparisons among the corresponding articles of 4 regions or countries were made in the order of IRB / IEC, Investigator, Sponsor and Clinical Trial Protocol. Based on the comparisons of the contents in ICH-GCP with those in current GCPs, major suggestions for the revision of current KGCP can be made as follows. Firstly, the function of IRB / IEC needs to be strengthened for the initiation and continuation of clinical trial. Current 2-step approval system of IRB / IEC and Health Authorities requires to be converted into the system similar to that of developed countries. Secondly, sponsor's obligation needs to be tightened to control and assure the quality of clinical trial. Inspection of regulatory authorities should be made to perform during and / or after clinical trial, when it is necessary. In other words, sponsor should be made to establish written Standard Operating Procedures (SOPs) for all aspects of clinical trial including monitoring to ensure that trials are conducted and data are generated, documented, and reported in compliance with the protocol, GCP, and the applicable regulatory requirement (s). Besides, the provision of ‘Quality Control and Quality Assurance’ should be added to the protocol to establish the credibility of the result of the clinical trial.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.187-192
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• 2008

A saturation and deadzone compensator is designed for robot systems using fuzzy logic (FL) and neural network (NN). The classification property of FL system and the function approximation ability of the NN make them the natural candidate for the rejection of errors induced by the saturation and deadzone. The tuning algorithms are given for the fuzzy logic parameters and the NN weights, so that the saturation and deadzone compensation scheme becomes adaptive, guaranteeing small tracking errors and bounded parameter estimates. Formal nonlinear stability proofs are given to show that the tracking error is small. The NN saturation and FL deadzone compensator is simulated on a robot system to show its efficacy.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.309-320
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• 2007

The data imbalance problem which can be uncounted in data mining classification problems typically means that there are more or less instances in a class than those in other classes. It causes low prediction accuracy of the minority class because classifiers tend to assign instances to major classes and ignore the minor class to reduce overall misclassification rate. In order to solve the data imbalance problem, there has been proposed a number of techniques based on resampling with replacement, adjusting decision thresholds, and adjusting the cost of the different classes. In this paper, we study the feasibility of the combination usage of the techniques previously proposed to deal with the data imbalance problem, and suggest a combination method using genetic algorithm to find the optimal combination ratio of the techniques. To improve the prediction accuracy of a minority class, we determine the combination ratio based on the F-value of the minority class as the fitness function of genetic algorithm. To compare the performance with those of single techniques and the matrix-style combination of random percentage, we performed experiments using four public datasets which has been generally used to compare the performance of methods for the data imbalance problem. From the results of experiments, we can find the usefulness of the proposed method.