• The Journal of the Korea Contents Association
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• v.17 no.9
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• pp.132-144
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• 2017

The fingerprint classification of categorizing fingerprints by classes should be used in order to improve the processing speed and accuracy in a fingerprint recognition system using a large database. The fingerprint classification methods extract features from the fingerprint ridges of a fingerprint and classify the fingerprint using learning and reasoning techniques based on the classes defined according to the flow and shape of the fingerprint ridges. In earlier days, many researches have been conducted using NIST database acquired by pressing or rolling finger against a paper. However, as automated systems using live-scan scanners for fingerprint recognition have become popular, researches using fingerprint images obtained by live-scan scanners, such as fingerprint data provided by FVC, are increasing. And these days the methods of fingerprint classification using Deep Learning have proposed. In this paper, we investigate the trends of fingerprint classification technology and compare the classification performance of the technology. We desire to assist fingerprint classification research with increasing large fingerprint database in improving the performance by mentioning the necessity of fingerprint classification research with consideration for fingerprint images based on live-scan scanners and analyzing fingerprint classification using deep learning.

• Journal of Soil and Groundwater Environment
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• v.19 no.2
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• pp.25-37
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• 2014

The volcanic type of geothermal water is linked intimately to active or potentially active volcanoes and takes place near the plate boundaries. In contrast to the volcanic type, the geothermal water in Korea has a non-volcanic origin. Korea's geothermal water is classified into the residual magma (RM) type and deep groundwater (DG) type according to the criterion of $35^{\circ}C$. This study reviewed the relationship between the physical and chemical features of the 281 geothermal water sources in South Korea in terms of the specific capacity, water temperature, and chemical compositions of two different basements (igneous rock and metamorphic rock) as well as the geological structures. According to the spatial relationship between the geothermal holes and geological faults, the length of the major fault is considered a key parameter determining the movement to a deeper place and the temperature of geothermal water. A negligible relationship between the specific capacity (Q/s) and temperature was found for both the RM type and DG type with the greater specific capacities of the RM- and DG-igneous types than the RM- and DG-metamorphic types. No relationship was observed between Q/s and the chemical constituents ($K^+$, $Na^+$, $Ca^{2+}$, $Mg^{2+}$, $Zn^{2+}$, $Cl^-$, $SO_4{^{2-}}$, $HCO_3{^-}$, and $SiO_2$) in the DG-igneous and DG-metamorphic types. Furthermore, weak relationship between temperature and chemical constituents was found for both the RM type and DG type.

• Animal cells and systems
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• v.16 no.3
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• pp.207-214
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• 2012

Burns are one of the most devastating forms of trauma and wound healing is a complex and multicellular process, which is executed and regulated by signaling networks involving numerous growth factors, cytokines, and chemokines. Recombinant human granulocyte macrophage colony-stimulating factor (rhGM-CSF) was specifically produced from rice cell culture through use of a recombinant technique in our laboratory. The effect of rhGM-CSF on promotion of deep second-degree burn wound healing on the back skin of a hamster model was evaluated through a randomized and double-blind trial. As macroscopic results, hamster skins of the experimental groups showed earlier recovery by new epidermis than the control groups. Immunohistochemical reactions of proliferating cell nuclear antigen and transforming growth factor-b1, which are indicators of cell proliferation, were more active in the experimental group, compared with the control group. On electron microscopy, basal cells in the epidermis of the experimental group showed oval nuclei, prominent nucleoli, numerous mitochondria and abundant free ribosomes. In addition, fibroblasts contained well-developed rough endoplasmic reticulum with dilated cisternae. Bundles of collagen fibrils filled the extracellular spaces. Particularly, ultrastructural features indicating active metabolism for regeneration of injured skin at 15 days after burn injury, including abundant euchromatin, plentiful free ribosomes, and numerous mitochondria, were observed. These findings suggest that use of rhGM-CSF could result in accelerated deep second-degree burn wound healing in animal models.

• The Journal of the Korea Contents Association
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• v.21 no.9
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• pp.105-116
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• 2021

Habruta is a question-based learning that talks, discusses, and argues in pairs. In particular, the famous painting Habruta is being implemented for the purpose of enhancing the appreciation ability of paintings and enriching the expressive power through questions and answers about the famous paintings. In this study, in order to support the famous painting Habruta for oriental paintings, we propose a method of automatically generating questions from the gender perspective of oriental painting characters using the current deep learning technology. Specifically, in this study, based on the pre-trained model, VGG16, we propose a model that can effectively analyze the features of Asian paintings by performing fine-tuning. In addition, we classify the types of questions into three types: fact, imagination, and applied questions used in the famous Habruta, and subdivide each question according to the character to derive a total of 9 question patterns. In order to verify the feasibilityof the proposed methodology, we conducted an experiment that analyzed 300 characters of actual oriental paintings. As a result of the experiment, we confirmed that the gender classification model according to our methodology shows higher accuracy than the existing model.

• Journal of the Korea Society of Computer and Information
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• v.26 no.11
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• pp.173-182
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• 2021

In this paper, we analyzed driver's and passenger's motions that cause driver's distraction, and recognized 10 driver's behaviors related to mobile phones. First, distraction-inducing behaviors were classified into environments and factors, and related recent papers were analyzed. Based on the analyzed papers, 10 driver's behaviors related to cell phones, which are the main causes of distraction, were recognized. The experiment was conducted based on about 100,000 image data. Features were extracted through SURF and tested with three models (CNN, ResNet-101, and improved ResNet-101). The improved ResNet-101 model reduced training and validation errors by 8.2 times and 44.6 times compared to CNN, and the average precision and f1-score were maintained at a high level of 0.98. In addition, using CAM (class activation maps), it was reviewed whether the deep learning model used the cell phone object and location as the decisive cause when judging the driver's distraction behavior.

• Journal of the Korea Convergence Society
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• v.9 no.12
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• pp.47-52
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• 2018

Named Entity Recognition is a system that extracts entity names such as Persons(PS), Locations(LC), and Organizations(OG) that can have a unique meaning from a document and determines the categories of extracted entity names. Recently, Bi-LSTM-CRF, which is a combination of CRF using the transition probability between output data from LSTM-based Bi-LSTM model considering forward and backward directions of input data, showed excellent performance in the study of object name recognition using deep-learning, and it has a good performance on the efficient embedding vector creation by character and word unit and the model using CNN and LSTM. In this research, we describe the Bi-LSTM-CNN-CRF model that enhances the features of the Korean named entity recognition system and propose a method for constructing the traditional culture corpus. We also present the results of learning the constructed corpus with the feature augmentation model for the recognition of Korean object names.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1239-1249
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• 2018

Optical remote sensing sensors provide visually more familiar images than radar images. However, it is difficult to discriminate sea ice types in optical images using spectral information based machine learning algorithms. This study addresses two topics. First, we propose a semantic segmentation which is a part of the state-of-the-art deep learning algorithms to identify ice types by learning hierarchical and spatial features of sea ice. Second, we propose a new approach by combining of semi-supervised and active learning to obtain accurate and meaningful labels from unlabeled or unseen images to improve the performance of supervised classification for multiple images. Therefore, we successfully added new labels from unlabeled data to automatically update the semantic segmentation model. This should be noted that an operational system to generate ice type products from optical remote sensing data may be possible in the near future.

• Journal of the Korea Convergence Society
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• v.10 no.10
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• pp.7-13
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• 2019

The natural gas (NG), mostly methane leaks into the air, it is a big problem for the climate. detected NG leaks under U.S. city streets and collected data. In this paper, we introduced a Deep Neural Network (DNN) classification of prediction for a level of NS leak. The proposed method is OrdinalEncoder(OE) based K-means clustering and Multilayer Perceptron(MLP) for predicting NG leak. The 15 features are the input neurons and the using backpropagation. In this paper, we propose the OE method for labeling target data using k-means clustering and compared normalization methods performance for NG leak prediction. There five normalization methods used. We have shown that our proposed OE based MLP method is accuracy 97.7%, F1-score 96.4%, which is relatively higher than the other methods. The system has implemented SPSS and Python, including its performance, is tested on real open data.

• The Journal of the Acoustical Society of Korea
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• v.38 no.1
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• pp.23-29
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• 2019

In this paper, a deep learning-based detection and classification using multi-band frequency signals is presented for detecting earthquakes prevalent in Korea. Based on an analysis of the previous earthquakes in Korea, it is observed that multi-band signals are appropriate for classifying earthquake signals. Therefore, in this paper, we propose a deep CNN (Convolutional Neural Network) using multi-band signals as training data. The proposed algorithm extracts the multi-band signals (Low/Medium/High frequency) by applying band pass filters to mel-spectrum of earthquake signals. Then, we construct three CNN architecture pipelines for extracting features and classifying the earthquake signals by a late fusion of the three CNNs. We validate effectiveness of the proposed method by performing various experiments for classifying the domestic earthquake signals detected in 2018.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.38.4-38.4
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• 2019

Ultra-diffuse galaxies (UDGs) are intriguing in the sense that they are much larger than dwarf galaxies but have much lower surface brightness than normal galaxies. To date, UDGs have been found only in the local universe. Taking advantage of deep and high-resolution HST images, we search for UDGs in massive galaxy clusters in the distant universe. In this work, we present our search results of UDGs in three massive clusters of the Hubble Frontier Fields: Abell 2744 (z=0.308), Abell S1063 (z=0.348), and Abell 370 (z=0.375). These clusters are the most distant and massive among the host systems of known UDGs. The color-magnitude diagrams of these clusters show that UDGs are mainly located in the faint end of the red sequence. This means that most UDGs in these clusters consist of old stars. Interestingly, we found a few blue UDGs, which implies that they had recent star formation. The radial number densities of UDGs clearly decrease in the central region of the clusters in contrast to those of bright galaxies which keep rising. This implies that a large fraction of UDGs in the central region were tidally disrupted. These features are consistent with those of UDGs in nearby galaxy clusters. We estimate the total number of UDGs (N(UDG)) in each cluster. The abundance of UDGs shows a tight relation with the virial masses (M_200) of thier host systems: M_200 \propto N(UDG)^(1.01+/-0.05). This slope is found to be very close to one, indicating that efficiency of UDGs does not significantly depend on the host environments. Furthermore, estimation of dynamical masses of UDGs indicates that most UDGs have dwarf-like masses (M_200 < 10^11 M_Sun), but a few UDGs have $L{\ast}$-like masses (M_200 > 10^11 M_Sun). In summary, UDGs in distant massive clusters are found to be similar to those in the local universe.