• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.12-20
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• 1998

As an approach to develope a control system with high robustness in changing control environment conditions, this paper will propose a robust control system, using multilayer neural network and biological immune system. The proposed control system adjusts weights of the multilayer neural network(MNN) with the immune algorithm. This algorithm is made up of two major divisions, the innate immune algorithm as a first line of defence and the adaptive immune algorithm as a barrier of self-adjustment. Using the proposed control system based on immune algorithm, we will work out a design for the controller of a robot manipulator. And we will demonstrate the effectiveness of the control system of robot manipulator with computer simulations.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.34 no.s01
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• pp.4-15
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• 1989

The objective of this paper is to compare and summarize the procedure and effectiveness of some bioassay systems and to point out ways to obtain reliable results from each bioassay. Detailed C:escriptions were given for those widely-adapted bioassay methods, such as mungbean rooting (auxin), Avena first internode straight growth (auxin), dwarf rice growth (gibberellin), dwarf pea epicotyl elongation (gibberellin), radish cotyledon expansion test (cytokinin), and tobacco stem pith callus growth (cytokinin), and the effects of various plant growth regulators including some recently introduced growth retardants (Paclobutrazol, Uniconazol, etc.) were also summarized.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.57-62
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• 2005

Microarray gene expression profiling technology is one of the most important research topics in clinical diagnosis of disease. Given thousands of genes, only a small number of them show strong correlation with a certain phenotype. To identify such an optimal subset from thousands of genes is intractable, which plays a crucial role when classify multiple-class genes express models from tumor samples. This paper proposes an efficient classifier design method to simultaneously select the most relevant genes using an intelligent genetic algorithm (IGA) and design an accurate classifier using Support Vector Machine (SVM). IGA with an intelligent crossover operation based on orthogonal experimental design can efficiently solve large-scale parameter optimization problems. Therefore, the parameters of SVM as well as the binary parameters for gene selection are all encoded in a chromosome to achieve simultaneous optimization of gene selection and the associated SVM for accurate tumor classification. The effectiveness of the proposed method IGA/SVM is evaluated using four benchmark datasets. It is shown by computer simulation that IGA/SVM performs better than the existing method in terms of classification accuracy.

• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1349-1364
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• 2006

An analytic approach that provides explicit estimates of risk on cataract and epilation data is evaluated by reasonableness of conceivable relative risk models regarding a simple, odds, logistic or Gompertz regression method, assuming a binomial distribution. In these analyses, we apply relative risk models with two thresholds between epilators and nonepilators from a highly characteristic lesion of which radiation cataract does not occur around 2 gray for a single acute exposure. The risk models are fitted to the data assuming 10 as a constant relative biological effectiveness of neutron. The likelihood of observing the entire data set in these models fitted is evaluated by an individual binary-response array. Estimation of a threshold with or without severe epilation and the 100 ($1-\alpha$)% confidence limits are derived from the maximum likelihood approach. The relative risk model with two thresholds can be expressed as a formula with structure of Background $\times$ RR, where RR includes threshold models with or without epilation. The radiosensitivity of ionizing radiation to cataracts has been examined for the relationship between epilators and nonepilators.

• Journal of Biomedical Engineering Research
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• v.31 no.2
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• pp.141-150
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• 2010

The purpose of this study was to analyze the golf swing motion for a soft golf clubs and regular golf club. Soft golf is a newly developed recreational sports for all ages, including the elderly and the beginners of golf. To quantify the effect of using soft golf club, which much lighter club than regular clubs, a motion analysis has been performed using a 3D optoelectric motion tracking system that utilizes active infrared LEDs and near-infrared sensors. The subject performed swing motion using a regular golf club and a soft golf club in turn. The obtained motion capture data was used to build a 3D computer simulation model to obtain left wrist, elbow shoulder and lumbar joint force and torque using inverse and forward dynamics calculations. The joint force and torque during soft golf swing were lower than regular golf swing. The analysis gave better understanding of the effectiveness of the soft golf club.

• Journal of Biomedical Engineering Research
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• v.38 no.4
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• pp.211-217
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• 2017

During the emergency situation such as cardiac arrest, cardiopulmonary resuscitation(CPR) is the most important treatment to maintain patient's blood circulation. Since the quality of CPR can not be easily measured or evaluated by the eye, an assistive device with an accelerometer can help to assess the pressure depth of CPR. In this study, we propose a single frequency analysis method to reduce the error of the accelerometer by extracting only one frequency component from the Fourier transform process. To verify the effectiveness of the single frequency analysis, acceleration data at CPR conditions were measured at a sampling rate of 50 / sec using a wristband equipped with an acceleration sensor. Then, We compared the existing distance estimation method and the single frequency analysis method using the measured data. The amplitude value proportional to the compression depth was obtained by applying the single frequency analysis method.

• Environmental Engineering Research
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• v.25 no.2
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• pp.205-211
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• 2020

Chlorhexidine digluconate (CHD) in the aquatic environment causes irreversible change to microbes, making them resistant to biodegradation, which needs remediation other than biological process. Adsorption study was performed for the removal of CHD on fly ash (FA) as a function of pH and ionic strength. Experimental result has been validated by characterization using Scanning electron microscopy, Fourier Transform-Infrared Spectroscopy and Brunauer-Emmett-Teller. CHD adsorption with FA showed an increasing trend with an increase in pH. Variation in pH proved to be an influential parameter for the surface charge of adsorbent and the degree of ionization of the CHD molecules. The adsorption capacity of CHD decreased from 23.60 mg g-1 to 1.13 mg g-1, when ionic strength increased from to M. The adsorption isotherms were simulated well by the Freundlich isotherm model having R2 = 0.98. The Lagergren's model was incorporated to predict the system kinetics, while the mechanistic study was better explained by pseudo-second order for FA. On the basis of operational conditions and cost-effectiveness FA was found to be more economical as an adsorbent for the adsorption of CHD.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.4
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• pp.75-84
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• 2014

In this paper, we dealt with feature selection problem of large-scale and high-dimensional biological data such as omics data. For this problem, most of the previous approaches used simple score function to reduce the number of original variables and selected features from the small number of remained variables. In the case of methods that do not rely on filtering techniques, they do not consider the interactions between the variables, or generate approximate solutions to the simplified problem. Unlike them, by combining set covering and clustering techniques, we developed a new method that could deal with total number of variables and consider the combinatorial effects of variables for selecting good features. To demonstrate the efficacy and effectiveness of the method, we downloaded gene expression datasets from TCGA (The Cancer Genome Atlas) and compared our method with other algorithms including WEKA embeded feature selection algorithms. In the experimental results, we showed that our method could select high quality features for constructing more accurate classifiers than other feature selection algorithms.

• Journal of Biomedical Engineering Research
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• v.28 no.5
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• pp.694-702
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• 2007

The adaptation effect originating from the chemical synapse between auditory nerve and inner hair cell gives advantage in accurate representation of temporal cues of incoming speech such as speech onset. Thus it is expected that the modification of conventional speech processing strategies of cochlear implant(CI) by incorporating the adaptation effect will result in considerable improvement of speech perception performance such as consonant perception score. Our purpose in this paper was to evaluate our new CI speech processing strategy incorporating the adaptation effect by the observation of auditory nerve responses. By classifying the presence or absence of speech from the auditory nerve responses, i. e. spike trains, we could quantitatively compare speech onset detection performances of conventional and improved strategies. We could verify the effectiveness of the adaptation effect in improving the speech onset representation characteristics.

• Journal of Biomedical Engineering Research
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• v.31 no.1
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• pp.40-49
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• 2010

A rule weight -based fuzzy classification model is proposed to analyze the patterns of admission-discharge of patients as a previous research for differential diagnosis of dyspnea. The proposed model is automatically generated from a labeled data set, supervised learning strategy, using three procedure methodology: i) select fuzzy partition regions from spatial distribution of data; ii) generate fuzzy membership functions from the selected partition regions; and iii) extract a set of candidate rules and resolve a conflict problem among the candidate rules. The effectiveness of the proposed fuzzy classification model was demonstrated by comparing the experimental results for the dyspnea patients' data set with 11 features selected from 55 features by clinicians with those obtained using the conventional classification methods, such as standard fuzzy classifier without rule weights, C4.5, QDA, kNN, and SVMs.