• Journal of KIISE
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• v.44 no.6
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• pp.587-594
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• 2017

Recently, motor imagery electroencephalogram(EEG) based Brain-Computer Interface(BCI) systems have received a significant amount of attention in various fields, including medicine and engineering. The Common Spatial Pattern(CSP) algorithm is the most commonly-used method to extract the features from motor imagery EEG. However, the CSP algorithm has limited applicability in Small-Sample Setting(SSS) situations because these situations rely on a covariance matrix. In addition, large differences in performance depend on the frequency bands that are being used. To address these problems, 4-40Hz band EEG signals are divided using nine filter-banks and Regularized CSP(R-CSP) is applied to individual frequency bands. Then, the Mutual Information-Based Individual Feature(MIBIF) algorithm is applied to the features of R-CSP for selecting discriminative features. Thereafter, selected features are used as inputs of the classifier Least Square Support Vector Machine(LS-SVM). The proposed method yielded a classification accuracy of 87.5%, 100%, 63.78%, 82.14%, and 86.11% in five subjects("aa", "al", "av", "aw", and "ay", respectively) for BCI competition III dataset IVa by using 18 channels in the vicinity of the motor area of the cerebral cortex. The proposed method improved the mean classification accuracy by 16.21%, 10.77% and 3.32% compared to the CSP, R-CSP and FBCSP, respectively The proposed method shows a particularly excellent performance in the SSS situation.

• 한국지구물리탐사학회:학술대회논문집
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• 2005.05a
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• pp.273-280
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• 2005

Robust magneto-telluric (MT) response function estimators are now in standard use in electromagnetic induction research. Properly devised and applied, these methods can reduce the influence of unusual data (outlier) in the response (electric field) variable, but often not sensitive to exceptional predictor (magnetic field) data, which are termed leverage points. A bounded influence estimator is described which simultaneously limits the influence of both outlier and leverage point, and has proven to consistently yield more reliable MT response function estimates than conventional robust approach. The bounded influence estimator combines a standard robust M-estimator with leverage weighting based on the statistics of the hat matrix diagonal, which is a standard statistical measure of unusual predictors. Further extensions to MT data analysis are proposed, including a establishment of data rejection criterion which minimize the influence of both electric and magnetic outlier in frequency domain based on statistical distribution of electromagnetic field. The rejection scheme made in this study seems to have an effective performance on eliminating extreme data, which is even not removed by BI estimator, in frequency domain. The effectiveness and advantage of these developments are illustrated using real MT data.

• Geophysics and Geophysical Exploration
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• v.11 no.1
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• pp.68-77
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• 2008

We have developed an inversion algorithm for loop-loop electromagnetic (EM) data, based on the localised non-linear or extended Born approximation to the solution of the 2.5D integral equation describing an EM scattering problem. Source and receiver configuration may be horizontal co-planar (HCP) or vertical co-planar (VCP). Both multi-frequency and multi-separation data can be incorporated. Our inversion code runs on a PC platform without heavy computational load. For the sake of stable and high-resolution performance of the inversion, we implemented an algorithm determining an optimum spatially varying Lagrangian multiplier as a function of sensitivity distribution, through parameter resolution matrix and Backus-Gilbert spread function analysis. Considering that the different source-receiver orientation characteristics cause inconsistent sensitivities to the resistivity structure in simultaneous inversion of HCP and VCP data, which affects the stability and resolution of the inversion result, we adapted a weighting scheme based on the variances of misfits between the measured and calculated datasets. The accuracy of the modelling code that we have developed has been proven over the frequency, conductivity, and geometric ranges typically used in a loop-loop EM system through comparison with 2.5D finite-element modelling results. We first applied the inversion to synthetic data, from a model with resistive as well as conductive inhomogeneities embedded in a homogeneous half-space, to validate its performance. Applying the inversion to field data and comparing the result with that of dc resistivity data, we conclude that the newly developed algorithm provides a reasonable image of the subsurface.

• Journal of Preventive Medicine and Public Health
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• v.21 no.2 s.24
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• pp.329-339
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• 1988

The study is carried out to investigate the effect of drinking and smoking for the activities of aspartate aminotransferase(AST, or GOT) and alanine aminotransferase(ALT or GPT), from December 25, 1986 to April 30, 1987. The male physical examinees for employment, 900 who had visited to the Taegu Medical Center were subjected. And the positive cases of HBs-Ag, Anti-HBs and skin test for Clonorchis sinensis were excluded. The general characters of drinking and smoking pattern were introduced by interview with questionnaire provided for. In drinking cases, the longer duration was significantly effected the higher rate of abnormality in AST and ALT level. But the amount and the frequency were not. It was not appeared effects by mackgulri which is a Korean traditional wine and small amount of beers. In smoking cases, also same pattern. The age was related in all cases. By the way, when the effect is related the positive results with other factors: HBs-Ag, Anti-HBs, skin test for Clonorchiasis and harmful occupational history, it is higher abnormal rate of AST and ALT in the duplicated cases with two factors or more. Particularly in HBs-Ag positive cases, those who had smoking was the highest in rate of abnormality, and drinking was the follows. In correlation matrix among seven factors; HBs-Ag, age, drinking amount, drinking period, drinking frequency, smoking amount and smoking period, correlation coefficient was significant between the abnormal rate and to with age, drinking period, smoking period, and smoking amount.

• The Journal of the Korea Contents Association
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• v.20 no.2
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• pp.162-171
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• 2020

The purpose of this study is to examine the social perception of disaster safety education for young children based on Textom big data and to explore the direction of young children's disaster safety education. Researchers collected and analyzed online text data using the keywords 'young children+disaster+safety education' from portal websites from 2014 to 2017. The raw data were then subjected to first and second data refinement process. Based on the frequency analysis results, 50 keywords were selected, and the selected keywords were converted into matrix data for network analysis. The results of the study are: first, the most frequently appeared keyword together with young children's disaster safety education was 'education', followed by 'experience', 'kindergarten', 'prevention', and 'school.' Second, keywords with high centrality in the analysis of centrality also were 'education', 'experience', and 'prevention'. In addition, keywords like 'prevention', 'life', and 'evacuation' appear higher in connection-centricity than frequency ranking, which means that the degree of connection between the words is high. These results suggest that young children need education in during early childhood in order to improve their disaster safety skills, and disaster safety education should be accomplished through 'prevention' and 'experience' in early childhood education institutions.

• The Korean Journal of Applied Statistics
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• v.30 no.4
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• pp.603-613
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• 2017

Many people have recently posted about personal interests on social media. The development of the Internet and computer technology has enabled the storage of digital forms of documents that has resulted in an explosion of the amount of textual data generated; subsequently there is an increased demand for technology to create valuable information from a large number of documents. A text mining technique is often used since text-based data is mostly composed of unstructured forms that are not suitable for the application of statistical analysis or data mining techniques. This study analyzed the Meteorological Yearbook data of the Korea Meteorological Administration (KMA) with a text mining technique. First, a term dictionary was constructed through preprocessing and a term-document matrix was generated. This term dictionary was then used to calculate the annual frequency of term, and observe the change in relative frequency for frequently appearing words. We also used regression analysis to identify terms with increasing and decreasing trends. We analyzed the trends in the Meteorological Yearbook of the KMA and analyzed trends of weather related news, weather status, and status of work trends that the KMA focused on. This study is to provide useful information that can help analyze and improve the meteorological services and reflect meteorological policy.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.11
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• pp.119-125
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• 1990

This paper describes fiber optic voltage sensor using EOM-BSO (Electro-Optic Modulator-Bismuth Silicon Oxcide). Transceiver has an electical/optical converter and an optical/electrical converter which consist of light emitting diode, PIN-PD, and electronic circuits. Multimode fiber cable of $100/140{\mu}m$ core/clad diameter is used for connecting the transceiver to fiber cable and fiber optic voltage sensor. Before our experiments, by applying the Maxwell equations and wave equations, We derive matrix equation on wave propagation in the BSO single crystal. And also we derive optimal equation on intensity modulation arising through an analyzer. According to experi-mental results, fiber optic voltage sensor has maximum $2.5{\%}$ error within the applied AC voltage of 800V. As the applied voltage increases, saturation values of voltage sensor also increase. This phenomenon is caused by optical rotatory power of BSO single crystal. And temperature dependence of sensitivity for fiber optical rotatory power of BSO single crystal. And temperature dependence of sensitivity for fiber optic voltage sensor in the temperature range from$-20^{\circ}C\to\60^{\circ}C$ are measured within ${\pm}0.6{\%}$. And frequency characteristics of the voltage sensor has good frequency characteristics from DC to 100kHz.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.40 no.11
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• pp.83-94
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• 2003

In this paper. a highly linear and low glitch CMOS current mode digital-to-analog converter (DAC) by self calibration bias circuit is proposed. The architecture of the DAC is based on a current steering 6＋4 segmented type and new switching scheme for the current cell matrix, which reduced non-linearity error and graded error. In order to achieve a high performance DAC . novel current cell with a low spurious deglitching circuit and a new inverse thermometer decoder are proposed. The prototype DAC was implemented in a 0.35${\mu}{\textrm}{m}$ n-well CMOS technology. Experimental result show that SFDR is 60 ㏈ when sampling frequency is 32MHz and DAC output frequency is 7.92MHz. The DAC dissipates 46 mW at a 3.3 Volt single power supply and occupies a chip area of 1350${\mu}{\textrm}{m}$ ${\times}$750${\mu}{\textrm}{m}$.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.27 no.1
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• pp.31-44
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• 2024

The coastal dune ecosystem is one of the ecosystems under the most development pressure in Korea. Therefore, it is necessary to study the ecological location and related ecological phenomena of coastal dune plants, but related studies are lacking. Through this study, we intend to conduct research on the structure and restoration of dune plants, focusing on the coastal dunes in Jeju Island, which are affected by artificial development pressure and the continuous increase in tourists among many coastal dunes in Korea. Ecosystems of coastal sand dunes for vegetation survey in Jeju Island are selected based on naturalness and preservation. In this study, 23 major coastal dunes on Jeju Island including Udo were selected. In the coastal dunes of Jeju Island, a whole species survey and quadrat survey were carried out. The vegetation survey at study sites were conducted on May to September 2022, when the vegetation is clearly visible. At the survey site, the dune area was identified at the beginning and the plant species were recorded until no more new species appeared. Vegetation survey in the field was performed by 103 quadrat establishments and was conducted using Braun-Blanquet method. A total of 277 species appeared, and the most common species were Vitex rotundifolia and Calystegia soldanella. The frequency of both Vitex rotundifolia and Calystegia soldanella was approximately over 90%. The proportion of woody and herbaceous in all emerging species was 7.2% and 92.8%, respectively. The total number of species found in the quadrat survey was 98. As a result of classifying plant communities based on species dominance in the quadrats, it was analyzed into 30 plant communities. The plant communities that appeared with a frequency of 2 or more on the main island of Jeju were Vitex rotundifolia, Imperata cylindrica var. koenigii, Ischaemum antephoroides, Wedelia prostrata, Elymus mollis, Calystegia soldanella, Artemisia scoparia, and Tetragonia tetragonoides. The DCCA(detrended canonical correspondence analysis) based on the vegetation and environment factor matrix showed that the height and covers of the dominant plant species explain significantly the variation and distribution of coastal sand dune species on Jeju island. Thus, we may propose a plan to restore the coastal dunes of Jeju island as helping colonization and establishment of mainly sand dune native perennials and trees, preserving native plant communities that are declining and preserving present tree strips of Pinus thunbergii, Litsea japonica, Pittosporum tobira and Vitex rotundifolia.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.181-193
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• 2024

The increasing atmospheric imbalance caused by climate change leads to an elevation in precipitation, resulting in a heightened frequency of flooding. Consequently, there is a growing need for technology to detect and monitor these occurrences, especially as the frequency of flooding events rises. To minimize flood damage, continuous monitoring is essential, and flood areas can be detected by the Synthetic Aperture Radar (SAR) imagery, which is not affected by climate conditions. The observed data undergoes a preprocessing step, utilizing a median filter to reduce noise. Classification techniques were employed to classify water bodies and non-water bodies, with the aim of evaluating the effectiveness of each method in flood detection. In this study, the Otsu method and Support Vector Machine (SVM) technique were utilized for the classification of water bodies and non-water bodies. The overall performance of the models was assessed using a Confusion Matrix. The suitability of flood detection was evaluated by comparing the Otsu method, an optimal threshold-based classifier, with SVM, a machine learning technique that minimizes misclassifications through training. The Otsu method demonstrated suitability in delineating boundaries between water and non-water bodies but exhibited a higher rate of misclassifications due to the influence of mixed substances. Conversely, the use of SVM resulted in a lower false positive rate and proved less sensitive to mixed substances. Consequently, SVM exhibited higher accuracy under conditions excluding flooding. While the Otsu method showed slightly higher accuracy in flood conditions compared to SVM, the difference in accuracy was less than 5% (Otsu: 0.93, SVM: 0.90). However, in pre-flooding and post-flooding conditions, the accuracy difference was more than 15%, indicating that SVM is more suitable for water body and flood detection (Otsu: 0.77, SVM: 0.92). Based on the findings of this study, it is anticipated that more accurate detection of water bodies and floods could contribute to minimizing flood-related damages and losses.