• Journal of the Korean Institute of Intelligent Systems
• /
• v.19 no.1
• /
• pp.54-61
• /
• 2009

This paper presents a new electromyogram (EMG) pattern recognition method based on the Hierarchical Temporal Memory (HTM) algorithm which is originally devised for image pattern recognition. In the modified HTM algorithm, a simplified two-level structure with spatial pooler, temporal pooler, and supervised mapper is proposed for efficient learning and classification of the EMG signals. To enhance the recognition performance, the category information is utilized not only in the supervised mapper but also in the temporal pooler. The experimental results show that the ten kinds of hand motion are successfully recognized.

• Journal of information and communication convergence engineering
• /
• v.9 no.6
• /
• pp.637-640
• /
• 2011

The hybrid neural networks have characteristics such as fast learning times, generality, and simplicity, and are mainly used to classify learning data and to model non-linear systems. The middle layer of a hybrid neural network clusters the learning vectors by grouping homogenous vectors in the same cluster. In the clustering procedure, the homogeneity between learning vectors is represented as the distance between the vectors. Therefore, if the distances between a learning vector and all vectors in a cluster are smaller than a given constant radius, the learning vector is added to the cluster. However, the usage of a constant radius in clustering is the primary source of errors and therefore decreases the recognition success rate. To improve the recognition success rate, we proposed the enhanced hybrid network that organizes the middle layer effectively by using the enhanced ART1 network adjusting the vigilance parameter dynamically according to the similarity between patterns. The results of experiments on a large number of calling card images showed that the proposed algorithm greatly improves the character extraction and recognition compared with conventional recognition algorithms.

• Journal of the Korea Society of Computer and Information
• /
• v.10 no.2 s.34
• /
• pp.113-121
• /
• 2005

In this paper, we design the hybrid learning algorithm of LVQ which is to perform EMG pattern recognition. The proposed hybrid LVQ learning algorithm is the modified Counter Propagation Networks(C.p Net. ) which is use SOM to learn initial reference vectors and out-star learning algorithm to determine the class of the output neurons of LVa. The weights of the proposed C.p. Net. which is between input layer and subclass layer can be learned to determine initial reference vectors by using SOM algorithm and to learn reference vectors by using LVd algorithm, and pattern vectors is classified into subclasses by neurons which is being in the subclass layer, and the weights which is between subclass layer and class layer of C.p. Net. is learned to classify the classified subclass. which is enclosed a class . To classify the pattern vectors of EMG. the proposed algorithm is simulated with ones of the conventional LVQ, and it was a confirmation that the proposed learning method is more successful classification than the conventional LVQ.

• Journal of the Korea Society of Computer and Information
• /
• v.11 no.5 s.43
• /
• pp.95-103
• /
• 2006

In this paper, the input pattern vectors are extracted and the learning algorithms is designed to recognize EEG pattern vectors. The frequency and amplitude of alpha rhythms and beta rhythms are used to compose the input pattern vectors. And the algorithm for EEG pattern recognition is used SOM to learn initial reference vectors and out-star learning algorithm to determine the class of the output neurons of the subclass layer. The weights of the proposed algorithm which is between the input layer and the subclass layer can be learned to determine initial reference vectors by using SOM algorithm and to learn reference vectors by using LVQ algorithm, and pattern vectors is classified into subclasses by neurons which is being in the subclass layer, and the weights between subclass layer and output layer is learned to classify the classified subclass, which is enclosed a class. To classify the pattern vectors of EEG, the proposed algorithm is simulated with ones of the conventional LVQ, and it was a confirmation that the proposed learning method is more successful classification than the conventional LVQ.

• Korean Journal of Acupuncture
• /
• v.33 no.3
• /
• pp.102-113
• /
• 2016

Objectives : This study is aimed at developing and discussing the prediction model of blood stasis pattern of traditional Korean medicine(TKM) using machine learning algorithms: multiple logistic regression and decision tree model. Methods : First, we reviewed the blood stasis(BS) questionnaires of Korean, Chinese, and Japanese version to make a integrated BS questionnaire of patient-reported outcomes. Through a human subject research, patients-reported BS symptoms data were acquired. Next, experts decisions of 5 Korean medicine doctor were also acquired, and supervised learning models were developed using multiple logistic regression and decision tree. Results : Integrated BS questionnaire with 24 items was developed. Multiple logistic regression models with accuracy of 0.92(male) and 0.95(female) validated by 10-folds cross-validation were constructed. By decision tree modeling methods, male model with 8 decision node and female model with 6 decision node were made. In the both models, symptoms of 'recent physical trauma', 'chest pain', 'numbness', and 'menstrual disorder(female only)' were considered as important factors. Conclusions : Because machine learning, especially supervised learning, can reveal and suggest important or essential factors among the very various symptoms making up a pattern identification, it can be a very useful tool in researching diagnostics of TKM. With a proper patient-reported outcomes or well-structured database, it can also be applied to a pre-screening solutions of healthcare system in Mibyoung stage.

• The Transactions of the Korea Information Processing Society
• /
• v.2 no.5
• /
• pp.619-625
• /
• 1995

This paper constructs a feed-forward learning complex algorithm which replaced by the backpropagation learning. This algorithm first attempts to organize the pattern vectors into clusters by Generalized Learning Vector Quantization(GLVQ) clustering algorithm(Nikhil R. Pal et al, 1993), second, regroup the pattern vectors belonging to different clusters, and the last, recognize into regrouping pattern vectors by single layer perceptron. Because this algorithm is feed-forward learning algorithm, time is less than backpropagation algorithm and the recognition rate is increased. We use 250 ASCII code bit patterns that is normalized to 16$\times$8. As experimental results, when 250 patterns devide by 10 clusters, average iteration of each cluster is 94.7, and recognition rate is 100%.

• Journal of Korean Home Economics Education Association
• /
• v.5 no.1
• /
• pp.45-59
• /
• 1993

The aims of this study are to develop CAI programs for bodice and sleeve pattern drafting for the 3rd grade students in a middle school and to compare the students’achievement degree in using traditional teaching method with that in using CAI programs. On the basis of results above the direction and possibility of using computers in education home economics is another aim. For this study, after the programs are developed, which is on the model of middle school text book, one hundred girl students of 2nd grade of middle school in Songpa Gu, Seoul, who have never given Bodice and Sleep Pattern Drafting lesson are chosen. The students are divided into two groups: one is called comparison group, which is taught by traditional method, the other, experimental group, which is taught by CAI programs. To evalute the learning achivement degree, evaluation test, effectiveness of learning and interest degree test and time test are given. Questionaire investigation about CAI program is given to experimental group. The results of this study are as follows: 1. CAI programs on pattern drafting are developed and can be used in the class. 2. In the tests of achievement degree, of effectiveness, and of interest the experimental group gets higher scores than the comparison group. 3. In the time test about how many students can draft the pattern in a given time, in every step more students in experimental group finished drafting than the comparison group. 4. The students showed interest in CAI programs and played an active part in the using computers. Therefore, using CAI programs in the class of pattern drafting saved the teachers lots of difficulties and enables individual learning improving the effectiveness of learning. I hope the studies of CAI will be established in other field as well as Home Economics, especially clothing part, and computers will be used lively

• The Journal of the Korea Contents Association
• /
• v.10 no.3
• /
• pp.463-471
• /
• 2010

The purpose of this study is to articulate learning model based on achievement level in blended learning environment. In order to investigate the variables and mechanisms in the blended learning environment, we started by attempt to develop two questionnaires using the components of web-based instruction and self-regulated learning. And its results were implemented to represent the topology and directed merging path within components. 154 students at a high school were required to take each web course respectively for two weeks. And questionnaires data, achievement levels data were collected and analyzed. Various statistical analysis methods such as correlation analysis, classical multidimensional scaling, multiple regression analysis, were applied to the data. As an result, the topology and directed path within factors of blended learning process were derived and revised as a final model.

• Journal of The Korean Association For Science Education
• /
• v.24 no.1
• /
• pp.121-128
• /
• 2004

This study was to investigate high students' conceptions of acids and bases, and their views on learning science. Multiple sources of data were collected over six months with a participation of sit tenth graders and their science teacher. The transcripts of interviews and other data were examined with an eye toward students' conceptions of acids and bases, and their views of learning science. Students' views of science are displayed the representative pattern. Each pattern is represented with an episode. Students' views of learning have been found to reflect the transmissive models of science educational practice. Students accept passive and difficult-to-modify views of the learner roles that they should play in the science classroom. Students identified science classes as conservative places, despite the introduction of science literacy as a goal of Korean science education since 1980. Behaviorism remains the major influence in their expectation, design, and practice in school science. Moreover, 'transmission' remains the persistent and dominant classroom cultural dynamic for both teaching and learning of science.

• Journal of Institute of Control, Robotics and Systems
• /
• v.3 no.6
• /
• pp.594-601
• /
• 1997

A new algorithm is proposed for training the multilayer perceptrion(MLP) in pattern classification problems to accelerate the learning speed. It is shown that the sigmoid activation function of the output node can have deterimental effect on the performance of learning. To overcome this detrimental effect and to use the information fully in supervised learning, an objective function for binary modes is proposed. This objective function is composed with two new output activation functions which are selectively used depending on desired values of training patterns. The effect of the objective function is analyzed and a training algorithm is proposed based on this. Its performance is tested in several examples. Simulation results show that the performance of the proposed method is better than that of the conventional error back propagation (EBP) method.