• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.6
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• pp.1283-1293
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• 2014

In ubiquitous environment, data are changing rapidly and new data is coming as times passes. And sometimes all of the past data will be lost if there is not sufficient space in memory. Therefore, there is a need to make rules and combine it with new data not to lose all the past data or to deal with large amounts of data. In making decision trees and extracting rules, the weight of each of rules is generally determined by the total number of the class at leaf. The computational problem of finding a minimum finite state acceptor compatible with given data is NP-hard. We assume that rules extracted are not correct and may have the loss of some information. Because of this precondition. this paper presents a new approach for refining rules. It controls their weight of rules of previous knowledge or data. In solving rule refinement, this paper tries to make a variety of rules with pruning method with majority and minority properties, control weight of each of rules and observe the change of performances. In this paper, the decision tree classifier with extended data expression having static weight is used for this proposed study. Experiments show that performances conducted with a new policy of refining rules may get better.

• Computational Structural Engineering
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• v.10 no.4
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• pp.217-228
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• 1997

The high precision analysis by the p-version of the finite element method are fairly well established as highly efficient method for linear elastic problems, especially in the presence of stress singularity. It has been noted that the merits of the p-version are accuracy, modeling simplicity, robustness, and savings in user's and CPU time. However, little has been done to exploit their benefits in elasto-plastic analysis. In this paper, the p-version finite element model is proposed for the materially nonlinear analysis that is based on the incremental theory of plasticity using the constitutive equation for work-hardening materials, and the associated flow rule. To obtain the solution of nonlinear equation, the Newton-Raphson method and initial stiffness method, etc are used. Several numerical examples are tested with the help of the square plates with cutout, the thick-walled cylinder under internal pressure, and the circular plate with uniformly distributed load. Those results are compared with the theoretical solutions and the numerical solutions of ADINA

• KIPS Transactions on Software and Data Engineering
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• v.11 no.10
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• pp.419-426
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• 2022

In this paper, we propose a method to improve the accuracy of 3D human pose estimation model in various move motions. Existing human pose estimation models have some problems of jitter, inversion, swap, miss that cause miss coordinates when estimating human poses. These problems cause low accuracy of pose estimation models to detect exact coordinates of human poses. We propose a method that consists of detection and correction methods to handle with these problems. Deep learning-based outlier detection method detects outlier of human pose coordinates in move motion effectively and rule-based correction method corrects the outlier according to a simple rule. We have shown that the proposed method is effective in various motions with the experiments using 2D golf swing motion data and have shown the possibility of expansion from 2D to 3D coordinates.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.9
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• pp.159-165
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• 1994

In this paper, we propose a multi-layer associative neural network structure suitable for hardware implementaion with the function of performance refinement and improved robutst capability. Unlike other methods which reduce network complexity by putting restrictions on synaptic weithts, we are imposing a requirement of hidden layer neurons for the function. The proposed network has synaptic weights obtainted by Hebbian rule between adjacent layer's memory patterns such as Kosko's BAM. This network can be extended to arbitary multi-layer network trainable with Genetic algorithm for getting hidden layer memory patterns starting with initial random binary patterns. Learning is done to minimize newly defined network error. The newly defined error is composed of the errors at input, hidden, and output layers. After learning, we have bidirectional recall process for performance improvement of the network with one-shot recall. Experimental results carried out on pattern recognition problems demonstrate its performace according to the parameter which represets relative significance of the hidden layer error over the sum of input and output layer errors, show that the proposed model has much better performance than that of Kosko's bidirectional associative memory (BAM), and show the performance increment due to the bidirectionality in recall process.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.28 no.4
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• pp.192-196
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• 2017

Social (pragmatic) communication disorder (SCD) is a new diagnosis included under communication disorders in the neurodevelopmental disorders section of Diagnostic and Statistical Manual of Mental Disorders-5. SCD is defined as a primary deficit in the social use of nonverbal and verbal communication. SCD has very much in common with pragmatic language impairment, which is characterized by difficulties in understanding and using language in context and following the social rules of language, despite relative strengths in word knowledge and grammar. SCD and Autism Spectrum Disorder (ASD) are similar in that they both involve deficits in social communication skills, however individuals with SCD do not demonstrate restricted interests, repetitive behaviors, insistence on sameness, or sensory abnormalities. It is essential to rule out a diagnosis of ASD by verifying the lack of these additional symptoms, current or past. The criteria for SCD are qualitatively different from those of ASD and are not equivalent to those of mild ASD. It is clinically important that SCD should be differentiated from high-functioning ASD (such as Asperger syndrome) and nonverbal learning disabilities. The ultimate goals are the refinement of the conceptualization, development and validation of assessment tools and interventions, and obtaining a comprehensive understanding of the shared and unique etiologic factors for SCD in relation to those of other neurodevelopmental disorders.

• Proceedings of the KIEE Conference
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• 1993.07b
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• pp.1247-1249
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• 1993

In this paper, NUDFET(NonUniformly Doped Field Effect Transistor) is presented as an alternative which offers the possibility of reducing the power necessary to operate switching circuits without a substantial loss in speed. The purpose of this NUDFET is to modify the electric field profile in order to cause carrier velocity saturation to occur at a lower voltage than it would occur in the uniformly doped device of the same channel length. The more MESFET and NUDFET circuits are realized, the more accurate model ins the performance of these devices become required. Analytic model ins was replaced by numerical analysis because of the complexity of device configuration. In this paper, FEM is selected because of simpler local mesh refinement and smaller computer memory than FDM. For accurate analysis, this paper has applied the Scharfetter-Gummel(S-G) Scheme and seven-point Gaussian Quadrature rule to assembly of the finite-element stiffness matrices and right-hand side vector of the semiconductor equations.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.91-97
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• 2021

This paper uses a data extension technique originally designed for the rule refinement problem to handling incomplete data. This technique is characterized in that each event can have a weight indicating importance, and each variable can be expressed as a probability value. Since the key problem in this paper is to find the probability that is closest to the missing value and replace the missing value with the probability, three different algorithms are used to find the probability for the missing value and then store it in this data structure format. And, after learning to classify each information area with the SVM classification algorithm for evaluation of each probability structure, it compares with the original information and measures how much they match each other. The three algorithms for the imputation probability of the missing value use the same data structure, but have different characteristics in the approach method, so it is expected that it can be used for various purposes depending on the application field.