• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.2
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• pp.165-172
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• 2010

In clinical data minig, choosing the optimal subset of features is such important, not only to reduce the computational complexity but also to improve the usefulness of the model constructed from the given data. Moreover the threshold values (i.e., cut-off points) of selected features are used in a clinical decision criteria of experts for differential diagnosis of diseases. In this paper, we propose a fuzzy discretization approach, which is evaluated by measuring the degree of separation of redundant attribute values in overlapping region, based on spatial distribution of data with continuous attributes. The weighted average of the redundant attribute values is then used to determine the threshold value for each feature and rough set theory is utilized to select a subset of relevant features from the overall features. To verify the validity of the proposed method, we compared experimental results, which applied to classification problem using 668 patients with a chief complaint of dyspnea, based on three discretization methods (i.e., equal-width, equal-frequency, and entropy-based) and proposed discretization method. From the experimental results, we confirm that the discretization methods with fuzzy partition give better results in two evaluation measures, average classification accuracy and G-mean, than those with hard partition.

• Journal of the Korea Society for Simulation
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• v.22 no.4
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• pp.139-147
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• 2013

This paper proposes a novel method for feature selection for mass spectrometric proteomic data based on Random Forest. The method includes an effective preprocessing step to filter a large amount of redundant features with high correlation and applies a tournament strategy to get an optimal feature subset. Experiments on three public datasets, Ovarian 4-3-02, Ovarian 7-8-02 and Prostate shows that the new method achieves high performance comparing with widely used methods and balanced rate of specificity and sensitivity.

• Journal of Fisheries and Marine Sciences Education
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• v.12 no.2
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• pp.176-190
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• 2000

It is possible to construct the more improved communication network quality due to practical use of data network using the redundant frequency bandwidth of CATV network. And the multimedia remote educations under the CATV network environment are being tried in the secondary schools. In general, CATV network is able to support not only the remote education using multimedia contents but also real-time responses because the network of CATV has capability to have transmission speed from 256Kbps to l0Mbps. In this paper, we design a new model of the efficient remote education by analysis of the multimedia data transmission capability using CATV network and suggest a method which can be applied specifically.

• Research in Community and Public Health Nursing
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• v.31 no.1
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• pp.24-33
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• 2020

Purpose: The purpose of this study was to examine the mediating effect of self-esteem and social support on the relationship between anxiety and health-related quality of life (HRQoL) in the elderly. Methods: The Korea adult psycho-social anxiety survey data were collected from August to September 2015 by the Korea Institute for Health. The subjects were 1,035 elderly people who were aged 65 or older at the time of the data survey. The data were analyzed by t-test, chi-square, Pearson correlation coefficient, and parallel redundant mediated model for PROCESS macro using SPSS 23.0. Results: They scored an average of 37.93±7.58 for anxiety, 28.59±3.45 for self-esteem, 17.25±4.11 for social support, and 0.88±0.11 for HRQoL. The direct effect of anxiety on HRQoL and the indirect effect of anxiety mediated with self-esteem and social support about HRQoL were statistically significant. Conclusion: These results indicate that in order to increase the HRQoL of the elderly, it is necessary to develop an intervention program that focuses not only on reducing anxiety but also on improving self-esteem and social support.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.10B
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• pp.1824-1831
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• 1999

In this paper, the data recovery performance of systematic erasure codes in burst loss environments is analyzed and the estimation method of redundant data according to loss characteristics is suggested. The burstness of packet loss is modeled by Gilbert model, and the performance of proposed packet loss recovery method in the case of using systematic erasure code is analyzed based on previous study on the loss recovery in the case of using erasure code. The required redundancy data fitting method for systematic erasure code in the condition of given loss property is suggested in the consideration of packet loss characteristics such as average packet loss rate and average loss length.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.89-92
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• 2003

It is often that a large-scale linear programming(LP) problem may contain many constraints which are redundant or cause infeasibility on account of inefficient formulation or some errors in data input. Presolving or preprocessing is a series of operations which removes the underlying redundancy or detects infeasibility in the given LP problem. It is essential for the speedup of an LP system solving large-scale problems to implement presolving techniques. For the recovery of an optimal solution for the original problem from an optimal solution for the presolved problem, a special procedure, so called postsolving, must be applied. In this paper, we present how a postsolving procedure is constructed and implemented in LPABO, a interior-point based LP system. Briefly, all presolving processes are logged in a data structure in LPABO, and after the end of the solution method an optimal solution for the original problem is obtained by tracing the logs. In each stage of the postsolving procedure, the optimality of intermediate solutions is maintained. We tested our postsolving procedure on Netlib, Gondzio and Kennington LP data sets, and concluded that the computational burden of the procedure is relatively negligible compared with the total solving time.

• The Korean Journal of Health Service Management
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• v.6 no.1
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• pp.219-229
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• 2012

The purpose of this study is to introduce the web database for healthy city Wonju that contains healthy city indicators and materials. It has provided diverse information to public officers who are working on healthy city projects and citizens for monitoring and evaluating the projects, effectively. The web database was made on 2006 and was updated on 2009. The new Web database system was designed for supporting that the staffs of healthy city can manage all data update by themselves. The new Web database encompasses more recent information about health city projects. After identifying users' needs and reasons for modifying the fields of data, we added new indicators to the Web database. Some redundant indicators were deleted based on users' requests. The Web database quality evaluations were performed by using 13 quality evaluations constructs. Through all 13 constructs, less than 20% of study subjects felt that it did not satisfy their needs or expectations. Well developed and verified contents of the Web database for healthy city are very essential and important. The database makes healthy city projects alive by managing and sharing healthy city related data and indicators effectively.

• Korean Journal of Remote Sensing
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• v.28 no.6
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• pp.611-622
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• 2012

This study proposed two multisensor fusion methods for segment-based image classification utilizing a region-growing segmentation. The proposed algorithms employ a Gaussian-PDF measure and an evidential measure respectively. In remote sensing application, segment-based approaches are used to extract more explicit information on spatial structure compared to pixel-based methods. Data from a single sensor may be insufficient to provide accurate description of a ground scene in image classification. Due to the redundant and complementary nature of multisensor data, a combination of information from multiple sensors can make reduce classification error rate. The Gaussian-PDF method defines a regional measure as the PDF average of pixels belonging to the region, and assigns a region into a class associated with the maximum of regional measure. The evidential fusion method uses two measures of plausibility and belief, which are derived from a mass function of the Beta distribution for the basic probability assignment of every hypothesis about region classes. The proposed methods were applied to the SPOT XS and ENVISAT data, which were acquired over Iksan area of of Korean peninsula. The experiment results showed that the segment-based method of evidential measure is greatly effective on improving the classification via multisensor fusion.

• Journal of Sensor Science and Technology
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• v.18 no.6
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• pp.432-440
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• 2009

This paper presents an accelerometer based system for physical activity decision that are capable of recognizing three different types of physical activities, i.e., standing, walking and running, using by rough sets. To collect physical acceleration data, we developed the body sensor node which consists of two custom boards for physical activity monitoring applications, a wireless sensor node and an accelerometer sensor module. The physical activity decision is based on the acceleration data collected from body sensor node attached on the user's chest. We proposed a method to classify physical activities using rough sets which can be generated rules as attributes of the preprocessed data and by constructing a new decision table, rules reduction. Our experimental results have successfully validated that performance of the rule patterns after removing the redundant attribute values are better and exactly same compare with before.

• ETRI Journal
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• v.27 no.6
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• pp.691-696
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• 2005

In asynchronous transfer mode (ATM) networks, fixed length cells of 53 bytes are transmitted. A cell may be discarded during transmission due to buffer overflow or a detection of errors. Cell discarding seriously degrades transmission quality. The quality degradation can be reduced by employing efficient forward error control (FEC) to recover discarded cells. In this paper, we present the design and implementation of decoding equipment for FEC in ATM networks based on a single parity check (SPC) product code using very-large-scale integration (VLSI) technology. FEC allows the destination to reconstruct missing data cells by using redundant parity cells that the source adds to each block of data cells. The functionality of the design has been tested using the Model Sim 5.7cXE Simulation Package. The design has been implemented for a $5{\times}5$ matrix of data cells in a Virtex-E XCV 3200E FG1156 device. The simulation and synthesis results show that the decoding function can be completed in 81 clock cycles with an optimum clock of 56.8 MHz. A test bench was written to study the performance of the decoder, and the results are presented.