• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.9
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• pp.540-546
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• 2003

A fast pattern classification algorithm with Cellular Nonlinear Network-based dynamic programming is proposed. The Cellular Nonlinear Networks is an analog parallel processing architecture and the dynamic programing is an efficient computation algorithm for optimization problem. Combining merits of these two technologies, fast pattern classification with optimization is formed. On such CNN-based dynamic programming, if exemplars and test patterns are presented as the goals and the start positions, respectively, the optimal paths from test patterns to their closest exemplars are found. Such paths are utilized as aggregating keys for the classification. The algorithm is similar to the conventional neural network-based method in the use of the exemplar patterns but quite different in the use of the most likely path finding of the dynamic programming. The pattern classification is performed well regardless of degree of the nonlinearity in class borders.

• Proceedings of the Korea Contents Association Conference
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• 2004.05a
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• pp.56-62
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• 2004

In the case that we do not have enough number of training patterns because of limitation such as time consuming, economic problem, and so on, we geneterate a new patterns using the brain-style Information processing algorithm, that is, supervised and unsupervised learning methods.

• Proceedings of the Korea Contents Association Conference
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• 2004.11a
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• pp.357-362
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• 2004

In the case that we do not have enough number of training patterns because of limitation such as time consuming, economic problem, and so on, we geneterate a new patterns using the brain-style information processing algorithm, that is, supervised and unsupervised learning methods. The proposed method is verified through the simulation of handwritten digits.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.6
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• pp.61-67
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• 2004

We propose a new algorithm to generate additional training patterns using the brain-style information processing algorithm, that is, supervised and unsupervised learning models. This will be useful in the case that we do not have enough number of training patterns because of limitation such as time consuming, economic problem, and so on. We adopt the independent component analysis as an unsupervised model for generating exempalr patterns and multilayer perceptions as supervised models for verifying usefulness of the generated patterns. After statistical analysis of the proposed pattern generation algorithm, we verify successful operations of our algorithm through simulation of handwritten digit recognition with various numbers of training patterns.

• The KIPS Transactions:PartB
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• v.13B no.2 s.105
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• pp.127-132
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• 2006

K-NN (k-Nearest Neighbors), which is a well-known instance-based learning algorithm, simply stores entire training patterns in memory, and uses a distance function to classify a test pattern. K-NN is proven to show satisfactory performance, but it is notorious formemory usage and lengthy computation. Various studies have been found in the literature in order to minimize memory usage and computation time, and NGE (Nested Generalized Exemplar) theory is one of them. In this paper, we propose RPA (Recursive Partition Averaging) and IRPA (Incremental RPA) which is an incremental version of RPA. RPA partitions the entire pattern space recursively, and generates representatives from each partition. Also, due to the fact that RPA is prone to produce excessive number of partitions as the number of features in a pattern increases, we present IRPA which reduces the number of representative patterns by processing the training set in an incremental manner. Our proposed methods have been successfully shown to exhibit comparable performance to k-NN with a lot less number of patterns and better result than EACH system which implements the NGE theory.

• Information Systems Review
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• v.18 no.1
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• pp.41-55
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• 2016

This empirical study discusses the current status and future directions of the SME informatization project based on cloud computing technology. Launched by the Korea Technology and Information Promotion Agency for SMEs in 2013, the project started with the exemplar support of seven cooperatives in the industry. Currently, understanding past usage patterns and user expectations is imperative in developing future strategies and implementation plans. We determined that user satisfaction and expectations are different between the generic basic solutions and the industry-specific solutions embedded in the industry-specific business processes. We propose several strategies on how to coordinate market concerns about government invention on the cloud software market and government support to invigorate the use of computer systems among SMEs.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.328-333
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• 2003

NGE (Nested Generalized Exemplars) proposed by Salzberg improved the storage requirement and classification rate of the Memory Based Reasoning. It constructs hyperrectangles during training and performs classification tasks. It worked not bad in many area, however, the major drawback of NGE is constructing hyperrectangles because its hyperrectangle is extended so as to cover the error data and the way of maintaining the feature weight vector. We proposed the OH (Optimizing Hyperrectangle) algorithm which use the feature weight vectors and the ED(Exemplar Densimeter) to optimize resulting Hyperrectangles. The proposed algorithm, as well as the EACH, required only approximately 40% of memory space that is needed in k-NN classifier, and showed a superior classification performance to the EACH. Also, by reducing the number of stored patterns, it showed excellent results in terms of classification when we compare it to the k-NN and the EACH.

• The Journal of Korean Obstetrics and Gynecology
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• v.25 no.1
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• pp.47-55
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• 2012

Purpose: Dysmenorrhea mostly depends on the causative factor, which usually falls under the categories of Deficiency and Excess pattern in traditional Oriental medical theories and diagnosis. Thus, we investigated menstruation symptom and sign related to dysmenorrhea and verified the validity of Deficiency and Excess pattern identification. Methods: We investigated menstruation symptom and sign related to dysmenorrhea in total 14 gynecology-medical books including the book ${\ll}$Exemplar Of Korean Medicine (Dongui Bogam)${\gg}$ and whole body symptom and sign identifying Deficiency and Excess pattern at the same time. A survey based on this investigation was carried out targeting women of childbearing age. Results: Total of 14 gynecology-medical books have mostly narrated pre-menstrual and mid & post-menstrual pelvic pain depending on the time of its manifestation for identifying Deficiency and Excess pattern. Dysmenorrhea in pre-menstrual period belonged to Excess pattern and dysmenorrhea in mid & post-menstrual period belonged to Deficiency pattern. Among a total of 343 women, 196 subjects suffered from dysmenorrhea. The number of dysmenorrhea in pre-menstrual period (Excess pattern) was 116 people and in mid & post-menstrual period (Deficiency pattern) was 80 people. Deficiency and Excess pattern of dysmenorrhea in menstrual period significantly correlated to Deficiency and Excess pattern of whole body symptom and sign in the statistics(P-value < 0.05). Conclusion: The results suggest that pre-menstrual and mid & post-menstrual pelvic pain depending on the time of its manifestation is preferentially utilized as symptom and sign related to dysmenorrhea identifying Deficiency and Excess pattern.