• The Research Journal of the Costume Culture
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• v.6 no.3
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• pp.201-216
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• 1998

This study is aimed at exploring a reasonable and reliable method of measuring pre-school children's somatotypes and there by, data basing the information obtained and classifying their somatotypes, at providing useful data which can be utilized for the design of their dress forms and enhancing the fitness of their apparels. to this end, 330 pre-school children living in the capital area and aged fro m4 to 6 were sampled to be subject to the measurement of their somatotypes. The results of this study can be summarized as follows; 1. As the pre-school children grow, the scales indicating their vertical growth including height could well be measured differently, but those scales indicating their lateral somatotypes which reflect their postural changes did not show among age groups. in other words, male kids were higher in the scales including height than female kids, while there were not differences between sexes in most scales indicating their lateral somatotypes. 2. The elements comprising the somatotypes were the size of body skeleton, the thickness of body mass, the posture and shape of body mass, the lateral under-neck shape, the extrusion of belly, the length between front and the back shoulder, the shape of lower belly, the shape of upper hip, the shape of lower hip and the slope of shoulders. Among them, the first two elements accounted for 64.8% of the total distribution, which means that these two elements explain the body-mass somatotypes of kid's most effectively. 3. The sample kids were divided into two types for classification of their somatotypes. As a result, it was found that the elements determining their somatotypes most influentially are, unlike adults' case the size of body skeleton rather than posture or lateral body shape. The type I showed less dimensions in most scales than type II, while their shoulder were les developed,. The type I was found distributed much in 4-year-old female kids. The type II showing more development in each element was found distributed much in 6-year-old male kids.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.4
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• pp.27-39
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• 2024

Soil texture is determined by the proportions of sand, silt, and clay within the soil, which influence characteristics such as porosity, water retention capacity, electrical conductivity (EC), and pH. Traditional classification of soil texture requires significant sample preparation including oven drying to remove organic matter and moisture, a process that is both time-consuming and costly. This study aims to explore an alternative method by developing an AI model capable of predicting soil texture from images of pre-sorted soil samples using computer vision and deep learning technologies. Soil samples collected from agricultural fields were pre-processed using sieve analysis and the images of each sample were acquired in a controlled studio environment using a smartphone camera. Color distribution ratios based on RGB values of the images were analyzed using the OpenCV library in Python. A convolutional neural network (CNN) model, built on PyTorch, was enhanced using Digital Image Processing (DIP) techniques and then trained across nine distinct conditions to evaluate its robustness and accuracy. The model has achieved an accuracy of over 80% in classifying the images of pre-sorted soil samples, as validated by the components of the confusion matrix and measurements of the F1 score, demonstrating its potential to replace traditional experimental methods for soil texture classification. By utilizing an easily accessible tool, significant time and cost savings can be expected compared to traditional methods.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.158-158
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• 2023

Characterizing the performance of precipitation (hereafter PRE) products in estimating the uncertainties in daily PRE in the era of global warming is of great value to the ecosystem's sustainability and human survival. This study intercompares the performance of different PRE products (gauge-based, satellite and reanalysis) sourced from the Frequent Rainfall Observations on GridS (FROGS) database over diverse climate zones in Africa and identifies regions where they depict minimal uncertainties in order to build optimal maps as a guide for different climate users. This is achieved by utilizing various techniques, including the triple collection (TC) approach, to assess the capabilities and limitations of different PRE products over nine climatic zones over the continent. For daily scale analysis, the uncertainties in light PRE (0.1 5mm/day) are prevalent over most regions in Africa during the study duration (2001-2016). Estimating the occurrence of extreme PRE events based on daily PRE 90th percentile suggests that extreme PRE is mainly detected over central Africa (CAF) region and some coastal regions of west Africa (WAF) where the majority of uncorrected satellite products show good agreement. The detection of PRE days and non-PRE days based on categorical statistics suggests that a perfect POD/FAR score is unattainable irrespective of the product type. Daily PRE uncertainties determined based on quantitative metrics show that consistent, satisfactory performance is demonstrated by the IMERG products (uncorrected), ARCv2, CHIRPSv2, 3B42v7.0 and PERSIANN_CDRv1r1 (corrected), and GPCC, CPC_v1.0, and REGEN_ALL (gauge) during the study period. The optimal maps that show the classification of products in regions where they depict reliable performance can be recommended for various usage for different stakeholders.

• Journal of the Korean Society of Earth Science Education
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• v.4 no.2
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• pp.102-107
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• 2011

In this study, elementary school science program, this category did not affect any troubleshooting analyzed. Thinking Science Program to buy for them in group activities by using one of the elements of a program of treatment and cognitive level effects were two kinds of research questions. 102, 5th grade four classes were involved, these two classes of the experimental group and the remaining two classes were divided into a control group. Pre-test between the two groups is compared to the level and classification problem-solving skills but the skills did not show a statistically significant difference. Thinking Science activity after application of classification and posttest the experimental group than in the control group problem solving abilities of students classified at the level of statistical significance was higher. Thinking Science program is a treatment effect for each level of analysis, tests, regardless of cognitive level was more effective. Through theses findings, Thinking Science activities 5th grade category classification problem-solving skills of students found to be effective in improving and these types of programs actively introduced in the field suggests that we need to see.

• Journal of Korea Multimedia Society
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• v.21 no.10
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• pp.1150-1161
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• 2018

Classification is widely used in medical images to categorize patients and non-patients. However, conventional classification requires a complex procedure, including some rigid steps such as pre-processing, segmentation, feature extraction, detection, and classification. In this paper, we propose a novel convolutional neural network (CNN), called LeukemiaNet, to specifically classify two different types of leukemia, including acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML), and non-cancerous patients. To extend the limited dataset, a PCA color augmentation process is utilized before images are input into the LeukemiaNet. This augmentation method enhances the accuracy of our proposed CNN architecture from 96.9% to 97.2% for distinguishing ALL, AML, and normal cell images.

• Journal of Korea Multimedia Society
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• v.23 no.11
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• pp.1406-1417
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• 2020

Sounds that have been evaluated to be effective in inducing sleep are helpful to reduce sleep disorders. Generally, several sounds have been verified the effects by brainwave experiments, but it cannot be considered on all users because of individual variation for effects. Moreover, the effectiveness for inducing sleep is not known for all new sounds made by creative activities. Therefore, new classification system is required to collect new effective sounds with considering personal brainwave characteristics. In this paper, we propose a new sound classification method by applying improved MinHash cluster to brain waves. The proposed method will classify them through whether it is effective for sleep care by evaluation his brainwave during listening for each sound. In order to prove effectiveness of the proposed classification method, we conducted accuracy experiment for sleep sound classification using verified sleep induction sound. In addition, we have compared time for existing method and proposed method. The former is scored 85% accuracy in the experiment. We confirmed the latter one that the average processing time was reduced to 70%. It is expected to be one of method for pre-screening whether it is effective when a new sound is introduced as a sound for sleep induction.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.3
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• pp.186-199
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• 2013

We pose pattern classification as a density estimation problem where we consider mixtures of generative models under partially labeled data setups. Unlike traditional approaches that estimate density everywhere in data space, we focus on the density along the decision boundary that can yield more discriminative models with superior classification performance. We extend our earlier work on the recursive estimation method for discriminative mixture models to semi-supervised learning setups where some of the data points lack class labels. Our model exploits the mixture structure in the functional gradient framework: it searches for the base mixture component model in a greedy fashion, maximizing the conditional class likelihoods for the labeled data and at the same time minimizing the uncertainty of class label prediction for unlabeled data points. The objective can be effectively imposed as individual mixture component learning on weighted data, hence our mixture learning typically becomes highly efficient for popular base generative models like Gaussians or hidden Markov models. Moreover, apart from the expectation-maximization algorithm, the proposed recursive estimation has several advantages including the lack of need for a pre-determined mixture order and robustness to the choice of initial parameters. We demonstrate the benefits of the proposed approach on a comprehensive set of evaluations consisting of diverse time-series classification problems in semi-supervised scenarios.

• Journal of Information Technology Services
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• v.21 no.5
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• pp.65-79
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• 2022

The smart factory has spread to small and mid-size enterprises (SMEs) under the leadership of the government. Smart factory consists of a work area, an operation management area, and an industrial control system (ICS) area. However, each site is combined with the IT system for reasons such as the convenience of work. As a result, various breaches could occur due to the weakness of the IT system. This study seeks to discover the items and vulnerabilities that SMEs who have difficulties in information security due to technology limitations, human resources, and budget should first diagnose and check. First, to compare the existing domestic and foreign smart factory vulnerability classification systems and improve the current classification system, the latest smart factory vulnerability information is collected from NVD, CISA, and OWASP. Then, significant keywords are extracted from pre-processing, co-occurrence network analysis is performed, and the relationship between each keyword and vulnerability is discovered. Finally, the improvement points of the classification system are derived by mapping it to the existing classification system. Therefore, configuration and maintenance, communication and network, and software development were the items to be diagnosed and checked first, and vulnerabilities were denial of service (DoS), lack of integrity checking for communications, inadequate authentication, privileges, and access control in software in descending order of importance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.655-657
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• 2021

It is challenging to find breast ultrasound image training dataset to develop an accurate machine learning model due to various regulations, personal information issues, and expensiveness of acquiring the images. However, studies targeting transfer learning for ultrasound breast cancer images classification have not been able to achieve high performance compared to radiologists. Here, we propose an improved transfer learning model for ultrasound breast cancer classification using publicly available dataset. We argue that with a proper combination of ImageNet pre-trained model and optimizer, a better performing model for ultrasound breast cancer image classification can be achieved. The proposed model provided a preliminary test accuracy of 99.5%. With more experiments involving various hyperparameters, the model is expected to achieve higher performance when subjected to new instances.

• Journal of the Korean School Mathematics Society
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• v.17 no.4
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• pp.493-506
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• 2014

This study is to seek access to a way of learning of the discipline of mathematics education, one of several knowledge is required to pre-service mathematics teachers. First, by selecting the key topics and researchers in mathematics education learning materials were produced by the relevant classification information by keyword. This applies to pre-service teachers in the curriculum, and looked to clarify the theoretically connectivity among the researchers and concepts and principles of the discipline of mathematics education. And as a result, investigate whether there is any effect to the pre-service teacher education.