• Journal of the Korean Society of Physical Medicine
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• v.15 no.2
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• pp.101-108
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• 2020

PURPOSE: The purpose of this study was to investigate the effect of treadmill walking training using the metronome on the gait pattern. METHODS: A total of 33 healthy persons were studied consisting of 17 female and 16 male in the 20-30 age group. A gait analysis program was installed on a treadmill with a built - in gait analysis sensor and laptop. After 9 minutes of treadmill walking, gait analysis was performed for 1 minute. The mean values of the differences in the step length, angle of COP, separation line standard deviation and step force of the lower legs affecting walking symmetry were calculated for treadmill walking and treadmill walking using the metronome. The Shapiro-Wilk test was used to test the normality of the collected data and a paired t-test was performed to analyze the difference in walking before and after using the metronome. RESULTS: As a result of the analysis, the mean of difference between the measured values of the bilateral lower extremity for step length, angle of COP, separation line standard deviation and step force were statistically significant before and after treadmill walking using the metronome. CONCLUSION: Therefore, the treadmill walking training using the metronome is effective in decreasing the difference in the foot width, gait angle, gait distribution, and foot pressure. Because of this, the treadmill walking training using the metronome has a significant effect on walking symmetry among the elements for correct walking, which is a means for enabling efficient and continuous walking.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.77-80
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• 2000

In this paper, the speaker-recognition process based on both DTW and discrete HMM was performed using the method to evaluate state-dependent parameter weighting from training data so as the personal audio-characteristics are to be well reflected. In the suggested method below, we found the optimal state sequence using the Viterbi algorithm. The optimal path could be evaluated after comparing the sequence of base pattern which already have, with that of the other patterns. After that the frame of which the pattern was matched with the base pattern in the same state are to be found so that the reference pattern can be gained by weighting on the numbers of matched frames.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.52 no.1
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• pp.35-41
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• 2003

In this paper, a machine vision system for recognizing and classifying the patterns and marks engraved by die molding or laser marking on the glass panels of computer monitors is suggested and evaluated experimentally. The vision system is equipped with a neural network and an NGC pattern classifier including searching process based on normalized grayscale correlation and adaptive binarization. This system is found to be applicable even to the cases in which the segmentation of the pattern area from the background using ordinary blob coloring technique is quite difficult. The inspection process is accomplished by the use of the NGC hypothesis and ANN verification. The proposed pattern recognition system is composed of three parts: NGC matching process and the preprocessing unit for acquiring the best quality of binary image data, a neural network-based recognition algorithm, and the learning algorithm for the neural network. Another contribution of this paper is the method of generating the training patterns from only a few typical product samples in place of real images of all types of good products.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.4
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• pp.27-43
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• 2005

This paper presents a new hybrid data mining technique using error pattern modeling to improve classification accuracy when the data type of a target variable is binary. The proposed method increases prediction accuracy by combining two different supervised learning methods. That is, the algorithm extracts a subset of training cases that are predicted inconsistently by both methods, and models error patterns from the cases. Based on the error pattern model, the Predictions of two different methods are merged to generate final prediction. The proposed method has been tested using practical 10 data sets. The analysis results show that the performance of proposed method is superior to the existing methods such as artificial neural networks and decision tree induction.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.216-221
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• 2004

This paper describes an optimal synthesis method of binary neural network for pattern recognition. Our objective is to minimize the number of connections and the number of neurons in hidden layer by using a Newly Expanded and Truncated Learning Algorithm (NETLA) for the multilayered neural networks. The synthesis method in NETLA uses the Expanded Sum of Product (ESP) of the boolean expressions and is based on the multilayer perceptron. It has an ability to optimize a given binary neural network in the binary space without any iterative learning as the conventional Error Back Propagation (EBP) algorithm. Furthermore, NETLA can reduce the number of the required neurons in hidden layer and the number of connections. Therefore, this learning algorithm can speed up training for the pattern recognition problems. The superiority of NETLA to other learning algorithms is demonstrated by an practical application to the approximation problem of a circular region.

• The Journal of the Korea Contents Association
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• v.5 no.5
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• pp.33-42
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• 2005

In this paper, we propose a new training algorithm for improving pattern classification performance of ART neural network. The proposed train algorithm restricts unnecessary cluster generation and transition, applies the location extraction algorithm, and operates the reset system based on the agreement between the present learning pattern and the initial pattern. As a result, repetitive input of a pattern does not generate a new cluster and mis-recognition rate decreases.

• Journal of the Korean Society of Earth Science Education
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• v.7 no.2
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• pp.276-285
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• 2014

The purpose of this study is to investigate the pattern and characteristics of elementary school teaching and learning processes in science based classes. The study participants' class was recorded in video and instructional conversation transcription. The pattern of the observed class was analyzed using the classification frame suggested by Mogan &Saxton(2006). In result, the questioning for elicit information was most frequent and questioning for shape understanding and the questioning for press for reflection in its priority. In result, the presence of elicited questioning for the attainment of knowledge and understanding is more prominent in science-based classrooms. It was revealed that the participating teachers used the questioning sentence pattern more frequently and the self-sustained inquiry that accelerates creative thinking of the student was lacking. It was discovered that teaching elicited questioning, which accelerates creative thinking, as well as fact confirmation pattern is a necessary element of training for teachers.

• Nuclear Engineering and Technology
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• v.36 no.3
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• pp.219-228
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• 2004

[1] and [2] present an approach to diagnosing possible defects in the mechanical systems of a nuclear power plant. In this paper, by using a fault library as a database and training data, we develop a diagnostic algorithm 1) to decide whether an Air Operated Valve system is sound or not and 2) to identify the defect from which an Air-Operated Valve system suffers, if any. This algorithm is composed of three stages: a neural net stage, a non-neural net stage, and an integration stage. The neural net stage is a simple perceptron, a pattern-recognition module, using a neural net. The non-neural net stage is a simple pattern-matching algorithm, which translates the degree of matching into a corresponding number. The integration stage collects each output and makes a decision. We present a simulation result and confirm that the developed algorithm works accurately, if the input matches one in the database.

• Atmosphere
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• v.17 no.4
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• pp.327-337
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• 2007

In this study, we examined the new ensemble training approach to reduce the systematic error and improve prediction skill of wind by using the Short-range Ensemble prediction system (SENSE), which is the mesoscale multi-model ensemble prediction system. The SENSE has 16 ensemble members based on the MM5, WRF ARW, and WRF NMM. We evaluated the skill of surface wind prediction compared with AWS (Automatic Weather Station) observation during the summer season (June - August, 2006). At first stage, the correction of initial state for each member was performed with respect to the observed values, and the corrected members get the training stage to find out an adaptive weight function, which is formulated by Root Mean Square Vector Error (RMSVE). It was found that the optimal training period was 1-day through the experiments of sensitivity to the training interval. We obtained the weighted ensemble average which reveals smaller errors of the spatial and temporal pattern of wind speed than those of the simple ensemble average.

• Speech Sciences
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• v.15 no.3
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• pp.103-116
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• 2008

Cerebral palsy children represent abnormal vocalization pattern caused by respiration problem and paralyzed oral motor muscle that are the basics of speech production. Thus, this study examined the effect of respiration and articulator training programs on the basic ability of speech production in CP children. The subjects of this study were 4 children with 3 of spastic CP and 1 of ataxia CP. The respiration and articulator program was conducted in 30 sessions for 30 minutes each. Pre-test was administered twice before the program, ongoing test was administered every 5 session during the period of experiment, and post-test was administered twice. The program included speech production such as respiration training, lips, jaw, cheek, and tongue exercise, and velopharyngeal training, and related articulator training. The following results were obtained. First, all subject children were less than 5 seconds in maximum phonation time before the experiment and 2 were improved by more than 4$\sim$5 seconds during the experiment, but 2 had relatively low rising width. Second, while children with less than 30dB before the experiment became bigger in strength during the experiment, children with more than 35dB before the experiment showed a minor change. Subject child 4 had lower vocal strength in the post-test period. Finally, although each subject had individual difference in syllable diadochokinetic ability, the function was improved and the number of repetition in one respiration was also increased.