• Korean Journal of Remote Sensing
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• v.29 no.6
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• pp.611-619
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• 2013

Although the need of crop production has increased in Mongolia, crop cultivation is very limited because of the harsh climatic and topographic conditions. Crop lands are sparsely distributed with relatively small sizes and, therefore, it is difficult to survey the exact area of crop lands. The study aimed to find an easy and effective way of accurate classification to map crop lands in Mongolia using satellite images. To classify the crop lands over the study area in northern Mongolia, four classifications were carried out by using 1) Thematic Mapper (TM) image August 23, 2) TM image of July 6, 3) combined 12 bands of TM images of July and August, and 4) both TM images of July and August by layered classification. Wheat and potato are the major crop types and they show relatively high variation in crop conditions between July and August. On the other hands, other land cover types (forest, riparian vegetation, grassland, water and bare soil) do not show such difference between July and August. The results of four classifications clearly show that the use of multi-temporal images is essential to accurately classify the crop lands. The layered classification method, in which each class is separated by a subset of TM images, shows the highest classification accuracy (93.7%) of the crop lands. The classification accuracies are lower when we use only a single TM image of either July or August. Because of the different planting practice of potato and the growth condition of wheat, the spectral characteristics of potato and wheat cannot be fully separated from other cover types with TM image of either July or August. Further refinements on the spatial characteristics of existing crop lands may enhance the crop mapping method in Mongolia.

• Convergence Security Journal
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• v.20 no.4
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• pp.81-90
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• 2020

In the real world, new types of attacks or variants are constantly emerging, but attack traffic classification models developed through artificial neural networks and supervised learning do not properly detect new types of attacks that have not been trained. Most of the previous studies overlooked this problem and focused only on improving the structure of their artificial neural networks. As a result, a number of new attacks were frequently classified as normal traffic, and attack traffic classification performance was severly degraded. On the other hand, the softmax function, which outputs the probability that each class is correctly classified in the multi-class classification as a result, also has a significant impact on the classification performance because it fails to calculate the softmax score properly for a new type of attack traffic that has not been trained. In this paper, based on this characteristic of softmax function, we propose an efficient method to improve the classification performance against new types of attacks by classifying traffic with a probability below a certain level as attacks, and demonstrate the efficiency of our approach through experiments.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.4
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• pp.22-30
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• 2008

In this paper, we deal with modulation classification method using WT capable of classifying incident digital signals without a priori information. These key features should have good properties of sensitive with modulation types and insensitive with SNR variation. The 4 key features for modulation recognition are selected using WT coefficients, which have the property of insentive to the changing of noise. The numerical simulations for classifying 8 digital modulation types using these features are peformed. The numerical simulations of the 3 types (i.e. DTC, MDC, and SVMC) of modulation classifiers are performed the investigation of classification accuracy and execution time to design the modulation classification module in software radio. The simulation result indicated that the execution time of MDC and DTC was best and MDC and SVMC showed good classification performance.

• Proceedings of the IEEK Conference
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• 2000.06e
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• pp.127-130
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• 2000

Classification of Electroencephalogram(EEG) makes one of key roles in the field of clinical diagnosis, such as detection for epilepsy. Spectrum analysis using the fourier transform(FT) uses the same window to signals, so classification rate decreases for nonstationary signals such as EEG's. In this paper, wavelet power spectrum method using wavelet transform which is excellent in detection of transient components of time-varying signals is applied to the classification of three types of Event Related Potential(EP) and compared with the result by fourier transform. In the experiments, two types of photic stimulation, which are caused by eye opening/closing and artificial light, are used to collect the data to be classified. After choosing a specific range of scales, scale-averaged wavelet spectrums extracted from the wavelet power spectrum is used to find features by Back-Propagation(13P) algorithm. As a result, wavelet analysis shows superiority to fourier transform for nonstationary EEG signal classification.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2014.05a
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• pp.160-161
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• 2014

This paper describes the classification problems of the unit modular housing through reviewing design drawings. Modular construction is a new industrialized homebuilding technology that uses large, factory produced modules. In general, more than 70% of construction processes in unit modular construction method are produced in factory after that the modular, which is completed is lifted on site and assembled. This research considers categorization in which the design drawing of the unit module housing can have problems. There are 17 types of construction method and 14 types of design approach as a result of the classification.

• Korean Journal of Remote Sensing
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• v.6 no.1
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• pp.11-24
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• 1990

This is the study about the meteorological satellite cloud image classification by objective methods. For objective cloud classification, linear discriminant analysis was tried. In the linear discriminant analysis 27 cloud characteristic parameters were retrieved from GMS infrared image data. And, linear cloud classification model was developed from major parameters and cloud type coefficients. The model was applied to GMS IR image for weather forecasting operation and cloud image was classified into 5 types such as Sc, Cu, CiT, CiM and Cb. The classification results were reasonably compared with real image.

• Journal of Korean Society of Rural Planning
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• v.11 no.2 s.27
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• pp.59-69
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• 2005

Because of increasing numbers of cars many highways are being constructed lively, and the noise of passing cars has influenced surrounding areas. In consideration of this, some alternatives and researches for soundproof facilities are proceeding, but aesthetic approach hasn't been considered. Therefore, this research is focused on soundproof effects for each types, effectual simulation methods, visual assessment and estimation between the landscape before simulation and the landscape after. Soundproof facilities are divided largely by the soundproof barrier, the soundproof mounding, the soundproof grove. The soundproof grove has three main function. First, leaves and branches absorbs sound vibrations. Second, leaves absorbs sound, and branches obstruct sounds. Third, by means of sounds of shaking leaves, forest can offset noises. This research was proceeded by means of classification of soundproof grove types and investigation of visual simulation methods. We made visual simulation for each types, and estimated the landscape for each types.

• Journal of the Korean Society of Costume
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• v.51 no.6
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• pp.167-176
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• 2001

This study analyzes characterization and classification of body types of Mongolian women aged 18∼39 ages. The anthropometric measurements of the research subjects come up to a total of 23 items and are summarized as follows : 1. As the results of comparative analysis of the body measurements by age group, 16 items show a significant difference except shoulder height, thigh girth, neck base girth, back length shoulder length, sleeve length and weight. Both age group are considered to be of average weight but 25 to 39 age group were slightly greater than that of the 18 to 24 age group. 2. As the results of factor analysis, 4 factors such as the first factor on the obesity of body, the second factor on the vertical size of body, the third factor on the back length, the forth factor on the shoulder width and neck base girth were extracted. 3. As the results of classification based on the duster analysis, the body types were classified into 3 types in each age group. In each age group the most frequent body type is average stature and slightly thin type.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.4
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• pp.589-593
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• 2013

Remote sensing of SAR images is suitable for sea ice observations to obtain the sea ice data if clouds or weather conditions change. There are various types of sea ice, classification results can be seen more easily to detect the change by types of sea ice. In this study, we classified the image by supervised classification method, which is minimum distance was used. Also, we compared the overall accuracy when compared to the results with classification result of SAR images and the result of texture images. When using Radarsat-2 texture images, the overall accuracy was the highest, generally, when using the SAR images had higher overall accuracy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.10
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• pp.1991-2000
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• 1994

In this paper, an alagorithm with six types of classification is suggested for the recognition system of hand-written Korean characters. After thinning process and truncating process for noise redection. The input images are used generalized by $64\times64$ size. The six type classification is composed of preliminary and secondary classification process by using the learning algoritm of multi-layer perceptron. Subblock Hough transform is used as local feature and sampling Hough transform is used as global feature. Experiment is conducted for 1800 characters which is written 31 times per each type by 10 persons. The 90% recognition rate is resulted by the preliminary classification of detection the final consonant and by the secondary classification of detecting the vowels.