• Mycobiology
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• v.43 no.3
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• pp.225-230
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• 2015

The polypore genus Abundisporus Ryvarden is characterized by resupinate to pileate fruitbodies with a purplish brown hymenophore, slightly thick-walled, pale yellowish and non-dextrinoid basidiospores, and causing white rot. A purple color hymenophore, an easily observable and striking character, was considered the main distinctive feature at the generic level within polypores. However, due to highly similar basidiocarp features, species identification within these purple polypores is particularly difficult. Three species of purple colored polypores have been reported in Korea (Abundisporus fuscopurpureus, A. pubertatis, and Fomitopsis rosea). Based on morphological re-examination, ecological information, and sequence analysis of the internal transcribed spacer, we showed that previous classification was incorrect and there is only one species (A. pubertatis) in Korea. We provide a detailed description of A. pubertatis in Korea, as well as a taxonomic key to distinguish wood rot fungi with a purple hymenophore.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.1045-1048
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• 2019

최근 CCTV 사용이 보편화되면서 방범 목적으로 서비스 시설이나 공공시설에 설치되는 CCTV의 수가 급격하게 증가하고 있다. 그에 따라 CCTV를 감시하는 노동력이 부족해지는 문제가 발생하여 이를 대체하기 위해 카메라 영상을 통하여 한번 인식한 사람을 다른 시간이나 장소에서 촬영된 영상에서 다시 인식하는 사람 재인식 기술이 주목받고 있다. 또한, 이러한 사람 재인식 기술은 보안 분야뿐만 아니라 영화나 드라마와 같은 영상 컨텐츠에 적용되어 불법 복제물을 찾는 일에 사용될 수도 있다. 기존의 사람 재인식에는 이미지의 유사도를 계산하는 방법이 사용되었지만, 조명이나 카메라 각도가 달라지면 성능이 급격하게 떨어지는 문제가 있었다. 최근에는 딥러닝 기술이 발달하면서 전반적인 영상처리 분야의 성능이 향상되었고, 사람 재인식 분야 역시 딥러닝을 활용하면서 성능이 향상되었다. 하지만 딥러닝을 활용한 방법의 경우 보통 두 개의 이미지를 입력으로 사용하여 같은지 다른지를 판단하게 되므로 각 이미지의 공통점이나 차이점을 동시에 고려하기는 어려운 점이 있다. 본 논문에서는 이러한 점을 해결하기 위해 세 개의 사람 이미지를 입력으로 사용하여 특징을 추출하고, 특징 맵을 재구성하여 각 이미지의 차이점과 공통점을 동시에 고려하며 학습할 수 있는 모델을 제안한다.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.665-675
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• 2019

The wire-wrapped fuel bundle is an assembly design in a sodium-cooled fast reactor. A wire spacer is used to maintain a constant gap between rods and to enhance the mixing of coolants. The wire makes the flow complicated by creating a sweeping flow and vortex flow. The vortex affects the flow field and heat transfer inside the subchannels. However, studies on vortices in this geometry are limited. The purpose of this research is to investigate the vortex flow created in the wire-wrapped fuel bundle. For analysis, a RANS-based numerical analysis was conducted for a 37-pin geometry. The sensitivity study shows that simulation with the shear stress transport model is appropriate. For the case of Re of 37,100, the mechanisms of onset, periodicity, and rotational direction were analyzed. The vortex structures were reconstructed in a three-dimensional space. Vortices were periodically created in the interior subchannel three times for one wire rotation. In the edge subchannel, the largest vortex occurred. This large vortex structure blocked the swirl flow in the peripheral region. The small vortex formed in the corner subchannel was negligible. The results can help in understanding the flow field inside subchannels with sweeping flow and vortex structures.

• Korean Journal of Plant Taxonomy
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• v.49 no.3
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• pp.203-208
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• 2019

Sparganium coreanum, a barely recognized species in Korea, was rediscovered during a field survey by the authors, who conducted a re-examination of specimens deposited in the Herbarium of the Korea National Arboretum (KH). This species was described initially by H. $L{\acute{e}}veill{\acute{e}}$ from a specimen collected by F. Taquet from Jeju-do (Taquet 2150). Subsequently, however, it was overlooked and unrecognized among South Korean flora. Several populations of S. coreanum were found in the southern part of the Korean Peninsula and on Jeju-do, although it has long been recognized as S. erectum owing to certain vegetative morphological characteristics shared between the two species, such as robust stems, a similar plant height, and globose rhizomes. However, it is distinct from S. erectum by the number of female heads on the lowest inflorescence branch and the size and shape of the fruit. In this study, we provide a detailed description, illustrations, and photographs with a revised taxonomic key for identification of Sparganium species in Korea.

• Genomics & Informatics
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• v.16 no.4
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• pp.23.1-23.5
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• 2018

Virus taxonomy was initially determined by clinical experiments based on phenotype. However, with the development of sequence analysis methods, genotype-based classification was also applied. With the development of genome sequence analysis technology, there is an increasing demand for virus taxonomy to be extended from in vivo and in vitro to in silico. In this study, we verified the consistency of the current International Committee on Taxonomy of Viruses taxonomy using an in silico approach, aiming to identify the specific sequence for each virus. We applied this approach to norovirus in Caliciviridae, which causes 90% of gastroenteritis cases worldwide. First, based on the dogma "protein structure determines its function," we hypothesized that the specific sequence can be identified by the specific structure. Firstly, we extracted the coding region (CDS). Secondly, the CDS protein sequences of each genus were annotated by the conserved domain database (CDD) search. Finally, the conserved domains of each genus in Caliciviridae are classified by RPS-BLAST with CDD. The analysis result is that Caliciviridae has sequences including RNA helicase in common. In case of Norovirus, Calicivirus coat protein C terminal and viral polyprotein N-terminal appears as a specific domain in Caliciviridae. It does not include in the other genera in Caliciviridae. If this method is utilized to detect specific conserved domains, it can be used as classification keywords based on protein functional structure. After determining the specific protein domains, the specific protein domain sequences would be converted to gene sequences. This sequences would be re-used one of viral bio-marks.

• Proceedings of the Korean Society of Crop Science Conference
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• 2018.10a
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• pp.179-179
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• 2018

Potato (Solanum tubersosum L.) is susceptible to various environmental stresses such as salt, high temperature, and drought. Especially, potato tuber growth is greatly affected by drought that causes not only yield reduction but also loss of tuber quality. Since unpredictable global weather changes cause more severe and frequent water limiting conditions, improvement of potato drought tolerance can minimize such adverse effects under drought and can impact on sustainable potato production. Genetic engineering can be utilized to improve potato drought tolerance, but such approaches using endogenous potato genes have rarely been applied. We were obtained AtbZIP28 gene transgenic potato plants. It is identified transcript levels at various stress conditions, polyethylene glycol (PEG), NaCl, (ABA). Also, For identification to regulate ER stress response genes in AtbZIP28 gene transgenic potato plant, we screened seven potato genes from RNA-seq analysis under TM treatment. Five and two genes were up- and down-regulated by TM, respectively. Their expression patterns were re-examined at stress agents known to elicit TM, DTT, DMSO and salt stress.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.147-165
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• 2021

In this work, we empirically evaluated the efficiency of the recent EfficientNetB0 model to identify and diagnose malaria parasite infections in blood smears. The dataset used was collected and classified by relevant experts from the Lister Hill National Centre for Biomedical Communications (LHNCBC). We prepared our samples with minimal image transformations as opposed to others, as we focused more on the feature extraction capability of the EfficientNetB0 baseline model. We applied transfer learning to increase the initial feature sets and reduced the training time to train our model. We then fine-tuned it to work with our proposed layers and re-trained the entire model to learn from our prepared dataset. The highest overall accuracy attained from our evaluated results was 94.70% from fifty epochs and followed by 94.68% within just ten. Additional visualization and analysis using the Gradient-weighted Class Activation Mapping (Grad-CAM) algorithm visualized how effectively our fine-tuned EfficientNetB0 detected infections better than other recent state-of-the-art DCNN models. This study, therefore, concludes that when fine-tuned, the recent EfficientNetB0 will generate highly accurate deep learning solutions for the identification of malaria parasites in blood smears without the need for stringent pre-processing, optimization, or data augmentation of images.

• Journal of Multimedia Information System
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• v.9 no.1
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• pp.1-10
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• 2022

China is a big country in animal fur industry. The total production and consumption of fur are increasing year by year. However, the recognition of fur in the fur production process still mainly relies on the visual identification of skilled workers, and the stability and consistency of products cannot be guaranteed. In response to this problem, this paper proposes a feature fusion-based animal fur recognition network on the basis of typical convolutional neural network structure, relying on rapidly developing deep learning techniques. This network superimposes texture feature - the most prominent feature of fur image - into the channel dimension of input image. The output feature map of the first layer convolution is inverted to obtain the inverted feature map and concat it into the original output feature map, then Leaky ReLU is used for activation, which makes full use of the texture information of fur image and the inverted feature information. Experimental results show that the algorithm improves the recognition accuracy by 9.08% on Fur_Recognition dataset and 6.41% on CIFAR-10 dataset. The algorithm in this paper can change the current situation that fur recognition relies on manual visual method to classify, and can lay foundation for improving the efficiency of fur production technology.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.5
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• pp.281-288
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• 2022

According to statistics conducted by the Korea Coast Guard, the number of accidents on small boats under 5 tons is increasing every year. This is because only a small number of people are on board. The previously developed maritime distress and safety systems are not well distributed because passengers must be equipped with additional remote equipment. The purpose of this study is to develop a distress awareness system that recognizes man over-board situations in real time. This study aims to present the part of the passenger tracking system among the small ship's distress awareness situational system that can generate passenger's location information in real time using deep learning based object detection and tracking technologies. The system consisted of the following steps. 1) the passenger location information is generated in the form of Bounding box using its detection model (YOLOv3). 2) Based on the Bounding box data, Deep SORT predicts the Bounding box's position in the next frame of the image with Kalman filter. 3) When the actual Bounding Box is created within the range predicted by Kalman-filter, Deep SORT repeats the process of recognizing it as the same object. 4) If the Bounding box deviates the ship's area or an error occurs in the number of tracking occupant, the system is decided the distress situation and issues an alert. This study is expected to complement the problems of existing technologies and ensure the safety of individuals aboard small boats.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.3
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• pp.213-220
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• 2023

Face recognition is a very important process in video monitoring and is a type of biometric technology. It is mainly used for identification and security purposes, such as ID cards, licenses, and passports. The recognition process has many variables and is complex, so development has been slow. In this paper, we proposed a face recognition method using CNN, which has been re-examined due to the recent development of computers and algorithms, and compared with the feature comparison method, which is an existing face recognition algorithm, to verify performance. The proposed face search method is divided into a face region extraction step and a learning step. For learning, face images were standardized to 50×50 pixels, and learning was conducted while minimizing unnecessary nodes. In this paper, convolution and polling-based techniques, which are one of the deep learning technologies, were used for learning, and 1,000 face images were randomly selected from among 7,000 images of Caltech, and as a result of inspection, the final recognition rate was 98%.