• Journal of Korean Elementary Science Education
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• v.24 no.2
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• pp.103-110
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• 2005

The purpose of this study was to investigate the relation between the learning styles and science process skills of students of the gifted class in elementary school. Subjects were forty-eight students of the gifted class who are in the fifth grade studying at the gifted class of S elementary school in Bucheon, M and Y elementary school in Incheon on learning styles and science process skills of students. Learning Style Profile (LSP) was used as instrument to survey learning style of students of the gifted class which was developed by NASSP, and consists of four categories (cognitive skills, perceptual response, orientation and teaming preferences) and twenty-four subscales. The results of this study were as follows: 1. In the learning styles test, students of the gifted class have higher scores of spatial skill, sequential processing skill, persistence orientation, manipulative preference, temperature preference and afternoon preference than general class students, but they have lower scores of discrimination skill and lighting preference, and there were statistically significant difference. 2. In science process skills test, there were statistically significant difference between students of the gifted class and general students. 3. In the correlation between the learning styles and science process skills, there was positive correlation of observing skill with spatial skill and manipulate skill of cognitive skill domain. For classifying skill, there was positive correlation with visual perceptual response, but was negative correlations with auditory and emotive perceptual response of perceptual response domain and with evening preference and verbal risk orientation of study preference domain. For measuring skill, there was positive correlation with sequential processing skill of cognitive skill domain. For formulating hypotheses, there was controlling variables, there was positive correlation with sequential processing skill and simultaneous processing skill of cognitive skill domain, and with verbal-spatial preference and early morning study preference of study preference domain. When planning and managing the gifted class, it will be beneficial and effective to consider the meaningful relations between the elements of loaming style and science process skills in order to improve science process skills.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.8
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• pp.307-324
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• 2018

Recently, recurrent neural networks have been attracting attention in solving prediction problem of sequential data through structure considering time dependency. However, as the time step of sequential data increases, the problem of the gradient vanishing is occurred. Long short-term memory models have been proposed to solve this problem, but there is a limit to storing a lot of data and preserving it for a long time. Therefore, research on memory-augmented neural network (MANN), which is a learning model using recurrent neural networks and memory elements, has been actively conducted. In this paper, we describe the structure and characteristics of MANN models that emerged as a hot topic in deep learning field and present the latest techniques and future research that utilize MANN.

• 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.

• The Journal of Korean Physical Therapy
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• v.23 no.3
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• pp.65-70
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• 2011

Purpose: We investigated the changes in the stop-signal reaction time (SSRT) and the no-signal reaction time (NSRT) following motor sequential learning in the stop-signal task (SST). This study also determined which of the reduction0s of spatial processing time was better between blocked- and random-SST. Methods: Thirty right-handed healthy subjects without a history of neurological dysfunction were recruited. In all subjects, both the SSRT and the NSRT were measured for the SST. Tasks were classified into two categories based on the stop-signal patterns, the blocked-SST practice group and random-SST practice group. All subjects gave written informed consent. Results: In the blocked-SST group, both the SSRT and the NSRT was significantly decreased (p<0.05) but not significantly changed in the random-SST group. In the SSRT and the NSRT, the blocked-SST group was faster than the random-SST group (p<0.05). In the post-test SST after practice of each group, the SSRT was significantly decreased in the random-SST group (p<0.05), but the NSRT showed no significant changes in either group. Conclusion: These findings demonstrate that random-SST practice resulted in a decrease in internal processing times needed for a rapid stop to visual signals, indicating motor skill learning is acquired through improved response selection and inhibition.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.1
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• pp.1-8
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• 2018

It is not easy to learn simple expressions of moving picture data since it contains noise and a lot of information in addition to time-based information. In this study, we propose a similarity representation method and a deep learning method between sequential data which can express such video data abstractly and simpler. This is to learn and obtain a function that allow them to have maximum information when interpreting the degree of similarity between image data vectors constituting a moving picture. Through the actual data, it is confirmed that the proposed method shows better classification performance than the existing moving image classification methods.

• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.267-274
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• 2021

Medical care practices include gathering a wide range of student data that are with manic episodes and depression which would assist the specialist with diagnosing a health condition of the students correctly. In this way, the instructors of the specific students will also identify those students and take care of them well. The data which we collected from the students could be straightforward indications seen by them. The artificial intelligence has been utilized with Naive Baye's classification, Random forest classification algorithm, SVM algorithm to characterize the datasets which we gathered to check whether the student is influenced by Bipolar illness or not. Performance analysis of the disease data for the algorithms used is calculated and compared. Also, a sequential deep learning model is builded using Keras. The consequences of the simulations show the efficacy of the grouping techniques on a dataset, just as the nature and complexity of the dataset utilized.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.653-662
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• 1989

Cerebellar Model Arithmetic Controller (CMAC) has been introduced as an adaptive control function generator. CMAC computes control functions referring to a distributed memory table storing functional values rather than by solving equations analytically or numerically. CMAC has a unique mapping structure as a coarse coding and supervisory delta-rule learning property. In this paper, learning aspects and a convergence of the CMAC were investigated. The efficient training algorithms were developed to overcome the limitations caused by the conventional maximum error correction training and to eliminate the accumulated learning error caused by a sequential node training. A nonlinear function generator and a motion generator for a two d.o.f. manipulator were simulated. The efficiency of the various learning algorithms was demonstrated through the cpu time used and the convergence of the rms and maximum errors accumulated during a learning process. A generalization property and a learning effect due to the various gains were simulated. A uniform quantizing method was applied to cope with various ranges of input variables efficiently.

• 한국기계연구소 소보
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• s.19
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• pp.93-115
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• 1989

Cerebellar Model Arithmetic Controller(CMAC) has been introduced as an adaptive control function generator. CMAC computes control functions referring to a distributed memory table storing functional values rather than by solving equations analytically or numerically. CMAC has a unique mapping structure as a coarse coding and supervisory delta-rule learning property. In this paper, learning aspects and a convergence of the CMAC were investigated. The efficient training algorithms were developed to overcome the limitations caused by the conventional maximum error correction training and to eliminate the accumulated learning error caused by a sequential node training. A nonlinear function generator and a motion generator for a two d. o. f. manipulator were simulated. The efficiency of the various learning algorithms was demonstrated through the cpu time used and the convergence of the rms and maximum errors accumulated during a learning process; A generalization property and a learning effect due to the various gains were simulated. A uniform quantizing method was applied to cope with various ranges of input variables efficiently.

• Journal of the Korea Society of Computer and Information
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• v.22 no.2
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• pp.81-87
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• 2017

Nowadays wine is increasingly enjoyed by a wider range of consumers, and wine certification and quality assessment are key elements in supporting the wine industry to develop new technologies for both wine making and selling processes. There have been many attempts to construct a more methodical approach to the assessment of wines, but most of them rely on objective decision rather than subjective judgement. In this paper, we propose a data mining approach to predict human wine taste preferences that is based on easily available analytical tests at the certification step. We used sequential forward selection and decision tree for this purpose. Experiments with the wine quality dataset from the UC Irvine Machine Learning Repository demonstrate the accuracies of 76.7% and 78.7% for red and white wines respectively.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.12
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• pp.2484-2491
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• 2009

This paper proposes robust state feedback control of asynchronous sequential machines with model uncertainty. The considered asynchronous machine is deterministic, but its state transition function is partially known before executing a control process. The main objective is to derive the existence condition for a corrective controller for which the behavior of the closed-loop system can match a prescribed model in spite of uncertain transitions. The proposed control scheme also has learning ability. The controller perceives true state transitions as it undergoes corrective actions and reflects the learned knowledge in the next step. An adaptation is made such that the controller can have the minimum number of state transitions to realize a model matching procedure. To demonstrate control construction and execution, a VHDL and FPGA implementation of the proposed control scheme is presented.