• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.83-83
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• 2016

가뭄은 발생과정과 피해영향에 따라서 기상학적, 농업적, 수문학적, 사회경제적 가뭄으로 구분되고 있다. 강수의 부족은 기상학적 가뭄을 발생시키고, 기상학적 가뭄이 지속되면 토양수분 부족으로 농작물의 피해를 가져오는 농업적 가뭄이 나타난다. 이어서, 지표 및 지표 아래의 수자원량이 평년수준 이하로 감소하면서 수문학적 가뭄이 발생한다. 이와 같이 다른 종류의 가뭄으로 가뭄이 변화하는 현상을 가뭄 전이(Drought Propagation)라고 한다. 강수량의 부족만으로 판단되는 기상학적 가뭄은 가뭄 상황의 판단은 간단지만, 체감으로 느끼는 가뭄과 차이가 존재한다. 수문학적 가뭄은 실제 물이용과 관련이 높아 효율적인 물 관리를 위해서는 수문학적 가뭄에 대한 정보가 필요하다. 하지만 수문학적 가뭄은 기상학적 인자들뿐만 아니라 수문순환과정의 영향을 받아 가뭄의 발생과정이 복잡하기 때문에 판단 및 예측이 어렵다. 본 연구에서는 가뭄의 전이관계를 도출함으로써, 기상학적 가뭄에서 수문학적 가뭄으로 발전되는 가뭄의 크기를 파악하고자 한다. 가뭄은 판단기준에 따라서 다양하게 정의될 수 있다. 본 연구에서는 기상학적 가뭄은 강우량으로, 수문학적 가뭄은 유역별 댐의 저수율, 유입량 및 지하수위를 활용하여 가뭄을 정의하며, 가뭄사상은 임계수준방법(threshold level approach)과 풀링기법(pooling method)을 수문기상 변수들에 적용하여 추출하였다. 수문학적 가뭄은 기상학적 가뭄이 발생한 이후, 가뭄 상황이 일정기간 지속되는 상황에 발생하는 결과가 확인되었다. 기상학적 가뭄에서 수문학적 가뭄이 전이되는 현상을 바탕으로 기상학적 가뭄의 상황에 따라서 미래의 수문학적 가뭄의 변화 양상에 대하여 예측가능하며, 가뭄의 전이관계는 수문학적 가뭄의 예측을 위한 자료로 활용될 수 있을 것으로 판단된다.

• Journal of Digital Convergence
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• v.17 no.10
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• pp.85-91
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• 2019

Present study notes that youth poverty is not only an income deficit, but also a deficit in various dimensions of life such as housing, work and health deficit. Multidimensional poverty is measured by four dimensions: income, work, housing and health. The sample is a 2630 one-person household female youth pooled from the Korea Welfare Panel 10-Year Data. The analysis tool used SPSS statistical program, and the analysis framework was the deficiency rate by dimension, the correlation analysis between deficiency dimension, and the overlapping rate of N dimension poverty. As a result, women's youth in Korea had higher deficit rate in terms of work and housing than other dimensions, and the proportion of women youth who were both poor in work and housing at the same time was also relatively higher than in other cases. Based on these results, this study proposes the construction of customized job services, job matching with small and medium-sized enterprises and allocation of one young woman's household among the targets of long-term chartered housing. Female youth's sharing-economy association should be considered as alternatives.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.5
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• pp.561-568
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• 2019

Recently, Korea has suffered from severe droughts due to climate change. Therefore, we need to pay attention to the change of drought risk to develop appropriate drought mitigation measures. In this study, we investigated the changes of hydrologic risk of extreme drought using the current observed data and the projected data according to the RCP 4.5 and 8.5 climate change scenarios. The bivariate frequency analysis was performed for the paired data of drought duration and severity extracted by the threshold level method and by eliminating pooling and minor droughts. Based on the hydrologic risk of extreme drought events Jeonbuk showed the highest risk and increased by 51 % than the past for the RCP 4.5 scenario, while Gangwon showed the highest risk and increased by 47 % than the past for the RCP 8.5 scenario.

• Journal of Convergence for Information Technology
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• v.7 no.2
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• pp.31-36
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• 2017

In this paper, we try to search and reproduce the image part of a specific cast from a large number of images. The conventional method must manually set the offset value when searching for a scene or viewing a corner. However, in this paper, the proposed method learns the main character 's face, then finds the main character in the image recognition and moves to the scene where the main character appears to reproduce the image. Data for specific performers is extracted and collected using crawl techniques. Based on the collected data, we learn using convolutional neural network algorithm and perform performance evaluation using it. The performance evaluation measures the accuracy by extracting and judging a specific performer learned in the extracted key frame while playing the drama. The performance confirmation of how quickly and accurately the learned scene is searched has obtained about 93% accuracy. Based on the derived performance, it is applied to the image service such as viewing, searching for person and detailed information retrieval per corner

• Journal of Veterinary Science
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• v.22 no.3
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• pp.37.1-37.14
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• 2021

Background: Ticks are one of the most common external parasites in dogs, and are associated with the transmission of a number of major zoonoses, which result in serious harm to human health and even death. Also, the increasing number of pet dogs and pet owners in China has caused concern regarding human tick-borne illnesses. Accordingly, studies are needed to gain a complete understanding of the bacterial composition and diversity of the ticks that parasitize dogs. Objectives: To date, there have been relatively few reports on the analysis of the bacterial community structure and diversity in ticks that parasitize dogs. The objective of this study was to investigate the microbial composition and diversity of parasitic ticks of dogs, and assessed the effect of tick sex and geographical region on the bacterial composition in two tick genera collected from dogs in China. Methods: A total of 178 whole ticks were subjected to a 16S ribosomal RNA (rRNA) next generation sequencing analysis. The Illumina MiSeq platform targeting the V3-V4 region of the 16S rRNA gene was used to characterize the bacterial communities of the collected ticks. Sequence analysis and taxonomic assignment were performed using QIIME 2 and the GreenGene database, respectively. After clustering the sequences into taxonomic units, the sequences were quality-filtered and rarefied. Results: After pooling 24 tick samples, we identified a total of 2,081 operational taxonomic units, which were assigned to 23 phyla and 328 genera, revealing a diverse bacterial community profile. The high, moderate and low prevalent taxa include 46, 101, and 182 genera, respectively. Among them, dominant taxa include environmental bacterial genera, such as Psychrobacter and Burkholderia. Additionally, some known tick-associated endosymbionts were also detected, including Coxiella, Rickettsia, and Ricketssiella. Also, the potentially pathogenic genera Staphylococcus and Pseudomonas were detected in the tick pools. Moreover, our preliminary study found that the differences in microbial communities are more dependent on the sampling location than tick sex in the tick specimens collected from dogs. Conclusions: The findings of this study support the need for future research on the microbial population present in ticks collected from dogs in China.

• Imaging Science in Dentistry
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• v.51 no.3
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• pp.299-306
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• 2021

Purpose: This study aimed to propose a fully automatic landmark identification model based on a deep learning algorithm using real clinical data and to verify its accuracy considering inter-examiner variability. Materials and Methods: In total, 950 lateral cephalometric images from Yonsei Dental Hospital were used. Two calibrated examiners manually identified the 13 most important landmarks to set as references. The proposed deep learning model has a 2-step structure-a region of interest machine and a detection machine-each consisting of 8 convolution layers, 5 pooling layers, and 2 fully connected layers. The distance errors of detection between 2 examiners were used as a clinically acceptable range for performance evaluation. Results: The 13 landmarks were automatically detected using the proposed model. Inter-examiner agreement for all landmarks indicated excellent reliability based on the 95% confidence interval. The average clinically acceptable range for all 13 landmarks was 1.24 mm. The mean radial error between the reference values assigned by 1 expert and the proposed model was 1.84 mm, exhibiting a successful detection rate of 36.1%. The A-point, the incisal tip of the maxillary and mandibular incisors, and ANS showed lower mean radial error than the calibrated expert variability. Conclusion: This experiment demonstrated that the proposed deep learning model can perform fully automatic identification of cephalometric landmarks and achieve better results than examiners for some landmarks. It is meaningful to consider between-examiner variability for clinical applicability when evaluating the performance of deep learning methods in cephalometric landmark identification.

• Smart Media Journal
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• v.8 no.3
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• pp.62-69
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• 2019

Electrocardiogram (ECG) signals cannot be counterfeited and can easily acquire signals from both wrists. In this paper, we propose a method of generating a coupling image using direction information of ECG signals as well as its usage in a personal recognition method. The proposed coupling image is generated by using forward ECG signal and rotated inverse ECG signal based on R-peak, and the generated coupling image shows a unique pattern and brightness. In addition, R-peak data is increased through the ECG signal calculation of the same beat, and it is thus possible to improve the recognition performance of the individual. The generated coupling image extracts characteristics of pattern and brightness by using the proposed convolutional neural network and reduces data size by using multiple pooling layers to improve network speed. The experiment uses public ECG data of 47 people and conducts comparative experiments using five networks with top 5 performance data among the public and the proposed networks. Experimental results show that the recognition performance of the proposed network is the highest with 99.28%, confirming potential of the personal recognition.

• The Journal of Information Systems
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• v.28 no.2
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• pp.53-72
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• 2019

Purpose The purpose of this study is to examine the impact of tie strength in the network of industrial clustering on the knowledge acquisition, integration and innovation performance of small and medium sized enterprises. We test the positive relationship of weak tie and knowledge acquisition, strong tie and knowledge integration, and the interaction effect of two tie strengths on both processes of knowledge acquisition and integration. By identifying these relationships, we can better understand how to manage the attributes of social networks in terms of tie strength in order to improve the performance of innovation for the small and medium sized enterprises. Design/methodology/approach We collect 200 survey data from 2 industrial cluster respectively: Pankyo and Guroo. In Pankyo, the proportion of IT industry is the highest (35%) while the proportion of manufacturing is highest (35%) in Guroo. Pooling the data from two industrial cluster, we check the reliability and validity of our research model and test the hypotheses. Findings First, we find the positive relationship of weak tie and knowledge acquisition from both industrial clustering. Weak tie is composed of heterogeneous organizations with various background and expertise. The communication and information sharing of organizations in the weak tie network helps the idea generation for organization's innovation, which is the knowledge acquisition process. Second, the relationship of strong tie and knowledge integration is insignificant. Typically the strong tie from long-lasting partnership is expected to be beneficial in the action stage of innovation, which is the knowledge integration process. However it is not identified in our industry cluster. Finally, the interaction effect of weak and strong tie is identified to be effective on both knowledge acquisition and integration processes.

• Korean Journal of Cognitive Science
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• v.30 no.1
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• pp.1-30
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• 2019

The main goal of this paper is to understand the underlying cognitive mechanisms of collaborative learning and how it can be supported by technology. The paper first overviews changes in conceptions of learning and distinguishes different types of joint works including collaborative learning. Cognitive mechanisms of collaborative learning are examined in terms of resource pooling, facilitation of constructive activities, knowledge co-construction, and supports for monitoring and regulation. Socio-emotional mechanisms of collaborative learning moderate the directions and strengths of these cognitive mechanisms. Technology supports these mechanisms in a number of different ways. Seven major supports technology provides for collaborative learning are discussed. A deeper understanding of the cognitive mechanisms of collaborative learning can help us to harness the benefits of collaborative learning better and also to develop more sophisticated tools supporting collaborative learning.

• Parasites, Hosts and Diseases
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• v.57 no.2
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• pp.161-166
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• 2019

This study was done to characterize distribution of Rickettsia spp. in ticks in the northwestern and southwestern provinces in the Republic of Korea. A total of 2,814 ticks were collected between May and September 2009. After pooling, 284 tick DNA samples were screened for a gene of Rickettsia-specific 17-kDa protein using nested PCR (nPCR), and produced 88 nPCR positive samples. Of these positives, 75% contained 190-kDa outer membrane protein gene (ompA), 50% 120-kDa outer membrane protein gene (ompB), and 64.7% gene D (sca4). The nPCR products of ompA, ompB, and sca4 genes revealed close relatedness to Rickettsia japonica, R. heilongjiangensis, and R. monacensis. Most Rickettsia species were detected in Haemaphysalis longicornis. This tick was found a dominant vector of rickettsiae in the study regions in the Republic of Korea.