• The Journal of the Acoustical Society of Korea
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• v.28 no.2
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• pp.135-140
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• 2009

In this paper, we synthesized active sonar target signals based on highlights model, and then carried out target classification using the synthesized signals. If the target aspect angle is changed, the different signals are synthesized. To know the result, two different experiments are done. First, The classification results with respect to each aspect angle are shown. Second, the results in two group in aspect angle are acquired. Time domain feature extraction is done using matched filter and envelope detection. It shows the pattern of each highlights. Artificial neural networks and multi-class SVM are used for classifying target signals.

• Journal of the Korean Society of Tobacco Science
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• v.5 no.2
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• pp.25-32
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• 1983

To obtain basic information on the breeding of burley tobacco, classification of 41 varieties was carried out by using the cluster analysis of correlation coefficients and taxonomic distance based on twenty-one agromonic characters. Eight characters, such as days to flowering, length of flower axis, internode length, leaf length, yield, leaf angle to stem, vein angle to midrib and plant height, were useful in monothetic classification. Forty-one varieties were classified into four groups (I, II, III and IV) with weighted variable group method (WVGM ) and weighted jai. group method(WPGM), whereas the results classification of 33 varieties among them by WVGM were coincident with the results by WPGM. As for the characteristics of each group, group I related to late maturity, tall height and high yield, group II related to intermediate maturity, tall height and low yield, group 19 related to early maturity, intermediate height and low yield, and group W related to early maturity, short height and intermediate yield.

• Korean Journal of Remote Sensing
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• v.25 no.6
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• pp.465-474
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• 2009

Convective/stratiform radar echo classification schemes by Steiner et al. (1995) and Biggerstaff and Listemaa (2000) are examined on a monsoonal front during the summer monsoon-Changma period, which is organized as a cloud cluster with mesoscale convective complex. Target radar is S-band with wavelength of 10cm, spatial resolution of 1km, elevation angle interval of 0.5-1.0 degree, and minimum elevation angle of 0.19 degree at Jindo over the Korean Peninsula. For verification of rainfall amount retrieved from the echo classification, ground-based rain gauge observations (Automatic Weather Stations) are examined, converting the radar echo grid data to the station values using the inverse distance weighted method. Improvement from the echo classification is evaluated based on the correlation coefficient and the scattered diagram. Additionally, an optimal use method was designed to produce combined rainfalls from the radar echo and Tropical Rainfall Measuring Mission Precipitation Radar (TRMM/PR) data. Optimal values for the radar rain and TRMM/PR rain are inversely weighted according to the error variance statistics for each single station. It is noted how the rainfall distribution during the summer monsoon frontal system is improved from the classification of convective/stratiform echo and the use of the optimal use technique.

• The Journal of the Acoustical Society of Korea
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• v.40 no.4
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• pp.330-336
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• 2021

In bistatic sonar operation, the scattering strength of a sonar target is characterized by the probe signal frequency, the aspect angle and the bistatic angle. Therefore, the target detection and identification performance of the bistatic sonar may vary depending on how the positions of the target, sound source, and receiver are changed during sonar operation. In this study, it was evaluated which variable is advantageous to change by comparing the target identification performance between the case of changing the aspect angle and the case of changing the bistatic angle during the operation. A scenario of identifying a hollow sphere and a cylinder was assumed, and performance was compared by classifying two targets with a support vector machine and comparing their accuracy using a finite element method-based acoustic scattering simulation. As a result of comparison, using the scattering strength defined by the frequency and the bistatic angle with the aspect angle fixed showed superior average classification accuracy. It means that moving the receiver to change the bistatic angle is more effective than moving the sound source to change the aspect angle for target identification.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.03a
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• pp.1365-1371
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• 2008

Based on the data obtained from field investigation and soil testing to slope hazards occurrence section and non-occurrence section in gneiss area, a prediction technique was developed by the use of a decision tree model. The slope hazards data of Seoul and Kyonggi Province were 104 sections in gneiss area. The number of data applied in developing prediction model was 61 sections except a vacant value. The statistical analyses using the decision tree model were applied to the entrophy index. As the results of analyses, a slope angle, a degree of saturation and an elevation were selected as the classification standard. The prediction model of decision tree using entrophy index is most likely accurate. The classification standard of the selected prediction model is composed of the slope angle, the degree of saturation and the elevation from the first choice stage. The classification standard values of the slope angle, the degree of saturation and elevation are $17.9^{\circ}$, 52.1% and 320m, respectively.

• The korean journal of orthodontics
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• v.2 no.1
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• pp.35-40
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• 1971

Since malocclusion affects a large segment of the population, it is by definition a public health problem. The etiology ana treatment of malocclusions have been studied by clinicians; however epidemioloic aspect of tile problem have been neglected. This study was undertaken using Angle's classification to obtain and to evaluate epidemiologic data on the prevalence of malocclusion in a group of 2,378 Yonsei University students, 17 to 23 years of age. All freshmen were selected, except for those students receiving orthodontic treatment and those few with too many missing teeth which prohibits classification by Angle's method. The following results were obtained: 1) Almost $91\%$ of students had malocclusion of the teeth severe enough to require correction. 2) There was a statistically significant difference in malocclusion between males and females($93.66\%$ malocclusion in males, $79.13\%$ malocclusioa in females). 3) Crowding was most pravalent in class I malocclusion. 4) There appeared to be a specific association between the number of lost first molars and Angle's classification. 5) In this study, more class II, Div.2 malocclusion appeared than in Massier's and Frankel's study of Caucasians, which used similar criteria. Class III malocclusion was more prevalent than normal occlusion in the Korean students studied, but in Caucasians' normal occlusion was more prevalent.

• International Journal of Naval Architecture and Ocean Engineering
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• v.4 no.4
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• pp.386-402
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• 2012

In this paper, a method to estimate ice loads as a function of the buttock angle of an icebreaker is presented with respect to polycrystalline freshwater ice. Ice model tests for different buttock angles and impact velocities are carried out to investigate ice pressure loads and tendencies of ice pressure loads in terms of failure modes. Experimental devices were fabricated with an idealized icebreaker bow shape, and medium-scale ice specimens were used. A dry-drop machine with a freefall system was used, and four pressure sensors were installed at the bottom to estimate ice pressure loads. An estimation equation was suggested on the basis of the test results. We analyzed the estimation equation for design ice loads of the International Association of Classification Societies (IACS) classification rules. We suggest an estimation equation considering the relation between ice load, buttock angle, and velocity by modifying the equations given in the IACS classification rules.

• Journal of Cadastre & Land InformatiX
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• v.46 no.2
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• pp.239-251
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• 2016

Hyperspectral image is widely used for land cover classification because it has a number of narrow bands and allow each pixel to include much more information in comparison with previous multi-spectral image. However, Higher spectral resolution of hyperspectral image results in an increase in data volumes and a decrease in noise efficiency. SAM(Spectral Angle Mapping), a method based on vector inner product to compare spectrum distribution, is a highly valuable and popular way to analyze continuous spectrum of hyperspectral image. SAM is shown to be less accurate when it is used to analyze hyperspectral image for land cover classification using spectral library. this inaccuracy is due to the effects of atmosphere. We suggest a decision tree based method to compensate the defect and show that the method improved accuracy of land cover classification.

• Journal of the Korea Society of Computer and Information
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• v.19 no.11
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• pp.43-52
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• 2014

In this paper, we propose the target detection method using image displacement, and classification method using SURF(Speeded Up Robust Features) feature points and BAS(Beam Angle Statistics) in infrared images. The SURF method that is a typical correspondence matching method in the area of image processing has been widely used, because it is significantly faster than the SIFT(Scale Invariant Feature Transform) method, and produces a similar performance. In addition, in most SURF based object recognition method, it consists of feature point extraction and matching process. In proposed method, it detects the target area using the displacement, and target classification is performed by using the geometry of SURF feature points. The proposed method was applied to the unmanned target detection/recognition system. The experimental results in virtual images and real images, we have approximately 73~85% of the classification performance.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.7
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• pp.644-649
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• 2012

This paper proposes the gait phase classifier using backpropagation neural networks method which uses the angle of lower body's joints and ground reaction force as input signals. The classification of a gait phase is useful to understand the gait characteristics of pathologic gait and to control the gait rehabilitation systems. The classifier categorizes a gait cycle as 7 phases which are commonly used to classify the sub-phases of the gait in the literature. We verify the efficiency of the proposed method through experiments.