• The Journal of Korean Physical Therapy
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• v.26 no.4
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• pp.234-239
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• 2014

Purpose: The purpose of this study is to use P300 latency to determine whether methods of motor learning in terms of massed and distributed practice can affect motor sequential learning in healthy adults. Methods: Twenty-four healthy subjects participated in this study. They were randomly allocated into three groups: a 10 minute, a 12 hour, and a 24 hour group. In the SRT task, eight numbers were adopted as auditory stimuli. During an experiment, participants were instructed to press the matching key as quickly and accurately as possible when one of the eight numbers was presented randomly. The subjects practiced for three sessions, each of which comprised five blocks of 40 serial reaction time tasks. While they practiced during these three sessions, P300 latency was measured. The data were analyzed using ANCOVA. Results: The P300 latency of Fz, Cz, and Pz decreased in all groups except for the Fz area of the 10 min group. Overall, the P300 latency of the 10 min group showed a smaller decrease compared with the 12 hr and 24 hr groups. Statistically, no significant differences in the Fz and Cz areas were observed among the three groups. The P300 latency in the Pz area of the 10 min group showed a significantly smaller decrease compared with the other groups. Conclusion: These findings suggest that short-term sequential motor training can alter brain functions such as the P300 latency. We also found that better acquisition of a motor skill was obtained with distributed practice of a task than with massed practice.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.10
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• pp.166-174
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• 1998

Neural networks, having highly nonlinear dynamics by virtue of the distributed nonlinearities and the learing ability, have the potential for the adaptive prediction of nonstationary signals. This paper describes the nonlinear prediction of these signals in two ways; using a nonlinear module and the cascade combination of nonlinear and linear modules. Fully-connected recurrent neural networks (RNNs) and a conventional tapped-delay-line (TDL) filter are used as the nonlinear and linear modules respectively. The dynamic behavior of the proposed predictors is demonstrated for chaotic time series adn speech signals. For the relative comparison of prediction performance, the proposed predictors are compared with a conventional ARMA linear prediction model. Experimental results show that the neural networks based adaptive predictor ourperforms the traditional linear scheme significantly. We also find that the cascade combination predictor is well suitable for the prediction of the time series which contain large variations of signal amplitude.

• Korean Journal of Social Welfare
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• v.65 no.1
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• pp.5-31
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• 2013

The principle objective of this study is to determine factors affecting social participation for physically disabled people with musculoskeletal conditions (spinal cord injury, muscular dystrophy, osteogenesis imperfecta, rheumatoid arthritis) in South Korea using ICF relevant categories of the international classification index by WHO. The subjects of this study 352 people with physical disabilities, the data were collected using ICF component(body functions, body structures, activities and participation and environmental factors) and the relevant categories deprived from ICF core sets. The collected data were evaluated with descriptive analysis, ANOVA, correlation analysis, and multiple regression analysis. The results of this study can be summarized as follow. The mental function, Neuromusculoskeletal and movement-related functions, Genitourinary and reproductive functions, Skin and related structures, Learning and applying knowledge, General tasks and demands, and Mobility positively influenced social participation in people with musculoskeletal conditions. However individual factors and environmental factors didn't statistically significant affect on social participation. The implications of the study is to examine by ICF components of universal approach on disability study and utilized the relevant ICF categories as measurement tools.

• The Journal of the Acoustical Society of Korea
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• v.23 no.7
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• pp.540-547
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• 2004

Since speech enhancement algorithms based on Kalman/Wiener filter require a priori knowledge of the noise and have focused on the minimization of the variance of the estimation error between clean and estimated speech signal, small estimation error on the noise statistics may lead to large estimation error. However, H/sub ∞/ filter does not require any assumptions and a priori knowledge of the noise statistics, but searches the best estimated signal among the entire estimated signal by applying least upper bound, consequently it is more robust to the variation of noise statistics than Kalman/Wiener filter. In this paper, we Propose a speech enhancement method using HMM and multi H/sub ∞/ filters. First, HMM parameters are estimated with the training data. Secondly, speech is filtered with multiple number of H/sub ∞/ filters. Finally, the estimation of clean speech is obtained from the sum of the weighted filtered outputs. Experimental results shows about 1dB∼2dB SNR improvement with a slight increment of computation compared with the Kalman filter method.

• Education of Primary School Mathematics
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• v.10 no.2
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• pp.125-139
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• 2007

The purpose of this study seeks entry into a method to make the use of educational aids popular. To achieve it, it is observed that instructions applying worksheets to make an activation of use of educational aids have influences on mathematical achievement and mathematical disposition and attitude. All variables exception with the frequence of use of educational aids are controlled in both experimental group and comparative group. According to the result, there is no significant difference of mathematical achievement in pre t-test between two groups, while experimental group get 10 points higher than comparative group in average (t=0.519, p<0.01). On the other hand, within intra-experimental group the influences of use of educational aids on mathematical achievement is positive without the achievement levels of students. The difference dependent on the levels of student is sought by ANCOVA using prescores as a covariance, and it appears in the significance level of 5%(F=4.885, p<0.05), and the effect is more in the lower level of students than in the middle and high level.

• The KIPS Transactions:PartD
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• v.8D no.6
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• pp.673-680
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• 2001

When the developers develop the software, the cost and time of the software development can be reduced by using blocks that are implemented previously. We call these implemented blocks components. In the early stage of Web-based Instruction, it didn't gain preference in spite of it's benefit of convenience. The main reason is, I think, the lack of generality at the education system which eventually results in unsatisfactory facilities compared with the requirement of teachers and students. And the early systems don't make good use of the plenty data in distributed environment, and don't show so good reliablity due to lack of systematic design and development. In this paper, we suggest WBI developing technology using the concept of WBSE. WBI developing is consist of component of pre-developed education software, integration of component using its reusability, and production of more requirement-satisfactory education software.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.10
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• pp.2180-2188
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• 2009

This paper is study on real-time hand gesture recognition system based on vision for intelligent robot control. We are proposed a recognition system using PCA and BP algorithm. Recognition of hand gestures consists of two steps which are preprocessing step using PCA algorithm and classification step using BP algorithm. The PCA algorithm is a technique used to reduce multidimensional data sets to lower dimensions for effective analysis. In our simulation, the PCA is applied to calculate feature projection vectors for the image of a given hand. The BP algorithm is capable of doing parallel distributed processing and expedite processing since it take parallel structure. The BP algorithm recognized in real time hand gestures by self learning of trained eigen hand gesture. The proposed PCA and BP algorithm show improvement on the recognition compared to PCA algorithm.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.10
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• pp.487-492
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• 2017

Clustering is a technique which is used to measure similarities between data in big data analysis and data mining field. Among various clustering methods, k-Modes algorithm is representatively used for categorical data. To increase the performance of iterative-centric tasks such as k-Modes, a distributed and concurrent framework Spark has been received great attention recently because it overcomes the limitation of Hadoop. Spark provides an environment that can process large amount of data in main memory using the concept of abstract objects called RDD. Spark provides Mllib, a dedicated library for machine learning, but Mllib only includes k-means that can process only continuous data, so there is a limitation that categorical data processing is impossible. In this paper, we design RDD for k-Modes algorithm for categorical data clustering in spark environment and implement an algorithm that can operate effectively. Experiments show that the proposed algorithm increases linearly in the spark environment.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.120-120
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• 2017

인천지역의 상수도공급은 팔당댐을 취수원으로 하여 도수, 송수관을 거쳐 인천지역 내 정수장을 통하여 각 급수지역까지 일원화된 관로시스템으로 공급되고 있다. 관망에서의 적절한 수압관리, 노후관로 교체사업 등은 급수관망 내 관로 사고위험을 줄일 수 있고, 누수량을 저감하여 무수율의 감소로 이어질 수 있다. 상수관망 내 누수에 영향을 주는 물리적, 운영적 요소를 파악하고, 이를 이용하여 누수해결을 위한 방법론을 제시하는 것은 매우 중요하다. 본 연구에서는 인천시 배수관망 데이터를 활용하여 통계분석 및 인공신경망을 통하여 무수율에 영향을 미치는 인자를 선별하고, 무수율과의 연관성을 분석하고자 하였다. 이를 위해 대상지역에 대한 시설현황 및 운영자료를 취득하고, 무수율 분석에 활용하였다. 인천시의 소블럭을 대상으로 관로노후도, 배수관연장, 평균관경, 급수전당 공급량, 누수발생 횟수, 용도지역, 관망구성 형태 등을 고려하여 무수율과의 관계분석을 위한 통계분석을 수행하였다. 특히 급수에 필요한 최소에너지와 관망에서 공급되는 에너지를 비교하기 위하여 관망해석 프로그램인 EPANET을 이용하여 관망내 절점에서의 수압과 수요량이 적용된 최소공급에너지를 활용하였고, 이를 통하여 블록 내 과잉공급에너지와 무수율의 영향성을 비교하였다. 최종적으로 산출된 주요인자에 대한 주성분분석, 분산분석, 다중회귀분석 등의 통계분석과 인공신경망에 의해 학습된 알고리즘을 통하여 산정된 무수율을 실측 무수율과 비교, 분석하였다. 인공신경망에 의해 산정된 무수율과 실측 무수율의 정확도를 평가하기 위하여 MAE, MSE, PBIAS 등의 정확도 평가와 산점도 분석을 수행하고, 상관계수를 도출하여 가장 정확한 방법을 결정하였다. 분석 결과 통계분석에 의한 다중회귀식으로 산출된 무수율 보다 인공신경망에 의한 무수율이 실측값에 더욱 근접한 것으로 나타났으며 이용된 뉴런의 수의 따라 산출결과가 상이하기 때문에 최적 뉴런의 수를 산정해야 할 필요가 있음을 확인하였다. 특히 사용된 상수관망 주요인자 중 주성분분석을 통하여 선정된 각 성분을 인공신경망에 적용시 더욱 정확한 무수율 예측이 가능한 것으로 나타났다.

• Journal of Convergence for Information Technology
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• v.9 no.1
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• pp.1-11
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• 2019

The study was to investigation the correlation between critical thinking and problem-solving abilities of 320 dental hygiene students enrolled in H university, J region from June 1 to August 30, 2018. The data was analyzed by ANOVA, pearson's correlation and multiple regression using SPSS 18.0 program. The correlation between critical score 3.36 while problem solving ability score 3.41. The correlation between critical thinking problem-solving abilities of the subjects was statistically significant. The significant variables included problem-solving(${\beta}=0.107$)(p<0.05), academic performance(${\beta}=-0.081$)(p<0.05) with an explanatory power of 52.2%. It is necessary to develop a curriculum and learning method in improvement for critical thinking and problem-solving abilities change in educational environment of dental hygiene students.