• Journal of Microbiology and Biotechnology
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• v.26 no.11
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• pp.1863-1870
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• 2016

Antimicrobial peptides/proteins (AMPs) are present in all types of organisms, from microbes and plants to vertebrates and invertebrates such as insects. The grasshopper Oxya chinensis sinuosa is an insect species that is widely consumed around the world for its broad medicinal value. However, the lack of available genetic information for this species is an obstacle to understanding the full potential of its AMPs. Analysis of the O. chinensis sinuosa transcriptome and expression profile is essential for extending the available genetic information resources. In this study, we determined the whole-body transcriptome of O. chinensis sinuosa and analyzed the potential AMPs induced by bacterial immunization. A high-throughput RNA-Seq approach generated 94,348 contigs and 66,555 unigenes. Of these unigenes, 36,032 (54.14%) matched known proteins in the NCBI database in a BLAST search. Functional analysis demonstrated that 38,219 unigenes were clustered into 5,499 gene ontology terms. In addition, 26 cDNAs encoding novel AMPs were identified by an in silico approach using public databases. Our transcriptome dataset and AMP profile greatly improve our understanding of O. chinensis sinuosa genetics and provide a huge number of gene sequences for further study, including genes of known importance and genes of unknown function.

• Journal of Korean Academy of Nursing
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• v.43 no.1
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• pp.1-10
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• 2013

Purpose: The purpose of this study was to develop a prediction model for the characteristics of older adults with depression using the decision tree method. Methods: A large dataset from the 2008 Korean Elderly Survey was used and data of 14,970 elderly people were analyzed. Target variable was depression and 53 input variables were general characteristics, family & social relationship, economic status, health status, health behavior, functional status, leisure & social activity, quality of life, and living environment. Data were analyzed by decision tree analysis, a data mining technique using SPSS Window 19.0 and Clementine 12.0 programs. Results: The decision trees were classified into five different rules to define the characteristics of older adults with depression. Classification & Regression Tree (C&RT) showed the best prediction with an accuracy of 80.81% among data mining models. Factors in the rules were life satisfaction, nutritional status, daily activity difficulty due to pain, functional limitation for basic or instrumental daily activities, number of chronic diseases and daily activity difficulty due to disease. Conclusion: The different rules classified by the decision tree model in this study should contribute as baseline data for discovering informative knowledge and developing interventions tailored to these individual characteristics.

• Journal of Preventive Medicine and Public Health
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• v.39 no.2
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• pp.177-183
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• 2006

Objectives : In this study we examined the association between the genetic markers ACE (A-240T, C-93T, I/D, A2350G), AGT (M235T), AT1R (A1166C), CYP11B2 (T344C, V386A), REN (G2646A), ADRB2 (G46A, C79G, T47C, T1641), GNB3 (C825T) and ADD1 (G460W) and the presence of essential hypertension in adolescents. Methods : The Kangwha Study is an 18-year prospective study that is aimed at elucidating the determinants of the blood pressure level from childhood to early adulthood. For this study, we constructed a case-control dataset of size of 277 and 40 family trios data from the Kangwha Study. For this purpose, we perform a single locus-based case-control association study and a single locus-based TDT (transmission/disequilibrium test) study. Results : In the case-control study, the single locus-based association study indicated that the ADD1 (G460W) (p=0.0403), AGT (M235T) (p=0.0002), and REN (G2646A) (p=0.0101) markers were significantly associated with the risk of hypertension. These results were not confirmed on the TDT study. This study showed that genetic polymorphisms of the ADD1, AGT and REN genes might be related to the hypertension in Korean adolescents. Conclusions : This study provided useful information on genetics markers related to blood pressure. Further study will be needed to confirm the effect of the alpha adducin gene, the angiotensinogen gene and the renin gene on essential hypertension.

• Korean Journal of Remote Sensing
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• v.21 no.2
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• pp.163-171
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• 2005

I have developed a processor for synthetic aperture radar (SAR) raw data compression using range-doppler algorithm for educational purpose. The program realized a generic SAR focusing algorithm so that it can deal with any SAR system if the specification is known. It can run efficiently on a low-cost computer by selecting minimum size out of a whole dataset, and can produce intermediate images during the process. Especially, the program is designed for educational purpose in such a way that Doppler centroid and azimuth ambiguity can be determined graphically by the user. By distributing the source code and the algorithm to public, I intend to maximize the educational effect on understanding and utilizing SAR data. This paper introduces the principle of SAR focusing algorithm embedded on the eSAR processor and shows an example of data processing using ERS-1 raw data.

• Journal of Convergence for Information Technology
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• v.11 no.9
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• pp.28-34
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• 2021

In the case of object detection using deep learning, both accuracy and real-time are required. However, it is difficult to use a deep learning model that processes a large amount of data in a limited resource environment. To solve this problem, this paper proposes an object detection model for small embedded devices. Unlike the general detection model, the model size was minimized by using a structure in which the pre-trained feature extractor was removed. The structure of the model was designed by repeatedly stacking lightweight convolution blocks. In addition, the number of region proposals is greatly reduced to reduce detection overhead. The proposed model was trained and evaluated using the public dataset PASCAL VOC. For quantitative evaluation of the model, detection performance was measured with average precision used in the detection field. And the detection speed was measured in a Raspberry Pi similar to an actual embedded device. Through the experiment, we achieved improved accuracy and faster reasoning speed compared to the existing detection method.

• Journal of Korea Trade
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• v.23 no.5
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• pp.129-144
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• 2019

Purpose - Recent empirical studies have reached mixed results on the effects of financial liberalization and currency crises. We argue that this relationship is likely to depend both on whether controls are primarily on the degrees of financial liberalization and on the stability of the government. Using the disaggregated data on financial liberalization recently developed by Abiad et al (2010) for a sample of 30 emerging countries over the period 1995-2015, we attempt to investigate the political economy determinants of currency crises. Design/methodology - Our empirical model considers the relationship between financial liberalization and currency crises for emerging market economies. This study employs the existing theoretical framework to identify the disaggregate level for financial liberalization across countries. Using a multivariate logit model, this study attempts to estimate the interrelationship among financial liberalization, government stability and currency crises complemented by a case study of South Korea. Findings - Our main findings can be summarized as follows: we find strong support for the proposition that more liberalized financial institutions are positively associated with the probability of currency crises especially under less stable governments, but reduce the risks of currency crises especially for more stable governments. We also examine the role of financial systems with the case of South Korea after Asian financial crises and the results are further supported and consistent with the empirical findings. Originality/value - Existing studies focus on the economic factors across countries. This paper instead attempts to evaluate the effects of financial liberalization and currency crises by incorporating political considerations with newly developed dataset on financial liberalization, which are essential to the understanding of the causes of currency crises.

• The Korean Journal of Archival Studies
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• no.52
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• pp.5-37
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• 2017

In consideration of the drastic transformation of records management environments, this study aims to suggest the directions for redesigning the electronic records management policies at a national level. First, it clarifies the four implicit objectives of electronic records management policies since the 2006 amendment of the Public Records Management Act, such as comprehensiveness for ensuring the appropriate management of any type of digital records, digital-friendly processes for records management, proper management for guaranteeing the evidential value of digital records, and long-term preservation of digital records. Second, it examines the challenging environmental factors in the areas since 2006. Third, it reviews the achievement of the policies as well as failures based on analyzing the policy documents and data from the National Archives of Korea. Fourth and finally, it suggests core areas and directions for redesigning the electronic records management policies, emphasizing the inclusiveness for data-type electronic records.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.1
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• pp.9-18
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• 2021

Although Generative Adversarial Networks (GANs) have gained great popularity in computer vision and related fields, generating audio signals independently has yet to be presented. Unlike images, an audio signal is a sampled signal consisting of discrete samples, so it is not easy to learn the signals using CNN architectures, which is widely used in image generation tasks. In order to overcome this difficulty, GAN researchers proposed a strategy of applying time-frequency representations of audio to existing image-generating GANs. Following this strategy, we propose an improved method for increasing the fidelity of synthesized audio signals generated by using GANs. Our method is demonstrated on a public speech dataset, and evaluated by Fréchet Inception Distance (FID). When employing our method, the FID showed 10.504, but 11.973 as for the existing state of the art method (lower FID indicates better fidelity).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.50-56
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• 2020

With the rapid development of deep neural network theory and application research, the effect of face detection has been improved. However, due to the complexity of deep neural network calculation and the high complexity of the detection environment, how to detect face quickly and accurately becomes the main problem. This paper is based on the relatively simple model of the MTCNN model, using FDDB (Face Detection Dataset and Benchmark Homepage), LFW (Field Label Face) and FaceScrub public datasets as training samples. At the same time of sorting out and introducing MTCNN(Multi-Task Cascaded Convolutional Neural Network) model, it explores how to improve training speed and Increase performance at the same time. In this paper, the dynamic image pyramid technology is used to replace the traditional image pyramid technology to segment samples, and OHEM (the online hard example mine) function in MTCNN model is deleted in training, so as to improve the training speed.

• The Journal of Korean Institute of Information Technology
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• v.17 no.12
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• pp.81-92
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• 2019

Weeds are a major object which is very harmful to crops. To remove the weeds effectively, we have to classify them accurately and use herbicides. As computing technology has developed, image-based machine learning methods have been studied in this field, specially convolutional neural network(CNN) based models have shown good performance in public image dataset. However, CNN with numerous training parameters and high computational amount. Thus, it works under high hardware condition of expensive GPUs in real application. To solve these problems, in this paper, a hierarchical architecture based deep-learning model is proposed. The experimental results show that the proposed model successfully classify 21 species of the exotic weeds. That is, the model achieve 97.2612% accuracy with a small number of parameters. Our proposed model with a few parameters is expected to be applicable to actual application of network based classification services.