• 셀메드
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• 제6권2호
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• pp.8.1-8.2
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• 2016

The purpose of this article is to argue that Arirang, Korean traditional music, could be used for healing purposes for Koreans. Music may be a medicine for curing both the body and mind. That is the soul of folksong. Arirang is a representative folksong in Korea. Koreans thought that listening to Korean traditional songs and singing them have healing powers because it makes people happier. When Koreans listen to Arirang, slowly as if they are mass hypnotized, Koreans calmed down because they think of mother's bosom while listening to Arirang. Also, Koreans find comfort in listening and singing to Arirang. The song's tune is catchy and its lyrics are moving. The song of Arirang was sung from long ago by Koreans. Therefore, it will continue forever as long as Koreans exist.

• 셀메드
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• 제6권2호
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• pp.10.1-10.3
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• 2016

Arirang has quite a long history in Korean music. Arirang means the heart is tingling. Jeongseon Arirang is well-known Arirang from the Gangwon province, so it shares certain features of Dongbu minyo (folksong), including the Menaritori. Jeongseon Arirang of Ok-sim Kim is pure and innocent. She has an ingenuous country voice. It is a kind of hope that provides our mental strength. Mental power is as important to our health as a eating right. Jeongseon Arirang sung by Ok-sim Kim is Seoul style of rhythmic pattern and melody. Jeongseon Arirang is designated as the Intangible Cultural Property No. 1 by the Gangwon province in 1971. This article outlines the emotional biography of Jeongseon Arirang of Ok-sim Kim relationship.

• 한국균학회소식:학술대회논문집
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• 한국균학회 2018년도 춘계학술대회 및 임시총회
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• pp.22-22
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• 2018

Fungal genome sequencing and assembly have been trivial in these days. Genome analysis relies on high quality of gene prediction and annotation. Automatic fungal genome annotation pipeline is essential for handling genomic sequence data accumulated exponentially. However, building an automatic annotation procedure for fungal genomes is not an easy task. FunGAP (Fungal Genome Annotation Pipeline) is developed for precise and accurate prediction of gene models from any fungal genome assembly. To make high-quality gene models, this pipeline employs multiple gene prediction programs encompassing ab initio, evidence-, and homology-based evaluation. FunGAP aims to evaluate all predicted genes by filtering gene models. To make a successful filtering guide for removal of false-positive genes, we used a scoring function that seeks for a consensus by estimating each gene model based on homology to the known proteins or domains. FunGAP is freely available for non-commercial users at the GitHub site (https://github.com/CompSynBioLab-KoreaUniv/FunGAP).

• 대한의용생체공학회:의공학회지
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• 제41권1호
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• pp.28-34
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• 2020

In this paper, we introduce motion artifact reduction algorithm for interleaved MRI using an advanced 3D approximation algorithm. The motion artifact framework of this paper is data corrected by post-processing with a new 3-D approximation algorithm which uses data structure for each voxel. In this study, we simulate and evaluate our algorithm using Shepp-Logan phantom and T1-MRI template for both scattered dataset and uniform dataset. We generated motion artifact using random generated motion parameters for the interleaved MRI. In simulation, we use image coregistration by SPM12 (https://www.fil.ion.ucl.ac.uk/spm/) to estimate the motion parameters. The motion artifact correction is done with using full dataset with estimated motion parameters, as well as use only one half of the full data which is the case when the half volume is corrupted by severe movement. We evaluate using numerical metrics and visualize error images.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2015년도 추계학술발표대회
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• pp.767-770
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• 2015

With the deployment of web-based educational game over the internet, the user's registration becomes a critical element. The user is authenticated by the system using username, password, and unique code. However, it cannot be handled properly because the data is transmitted through insecure channel on the network. Hence, security requirement is needed to avoid identity leakage from malicious user. In this paper, we propose a secure communication approach using SSL protocol for an online game. We also describe the security requirements for our approach. In future work, we intend to configure and implement the SSL protocol by enabling HTTPS in web-based online game.

• 천문학회보
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• 제44권1호
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• pp.47.3-47.3
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• 2019

We discovered 722 open clusters within 1 kpc using Gaia DR2 data. These clusters are detected in the proper motion space and confirmed on the spatial distribution with parallax information. We divided the 3628 regions and visually searched using python program. Among 722 open clusters, 430 clusters are previously unknown clusters. Catalogue of discovered clusters is unloaded on the online catalogue at https://radio.kasi.re.kr/project/shlee/. Owing to the good membership criteria, we could see the halo structure of the clusters. In that reason, the average size of the discovered cluster is about 9 times than that of previously known clusters.

• 천문학회보
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• 제43권2호
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• pp.53.1-53.1
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• 2018

We present the second data release from the SAMI Galaxy Survey. The data release contains reduced spectral cubes for 1559 galaxies, about 50% of the full survey, having a redshift range 0.004 < z < 0.113 and a large stellar mass range 7.5 < log($M_*/M_{\odot}$) < 11.6. This release also includes stellar kinematic and stellar population value-added products derived from absorption line measurements, and all emission line value-added products from Data Release One. The data are provided online through Australian Astronomical Optics' Data Central. Our poster presents stellar/gas kinematics on the metallicity-mass plane and highlight several galaxies from the SAMI Galaxy Survey that have interesting stellar and gas kinematics. For more information about data release 2, please see: https://sami-survey.org/abdr.

• 한국IT서비스학회지
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• 제18권1호
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• pp.173-186
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• 2019

Recently, as the amount of fine dust has risen rapidly, our interest is increasing day by day. It is virtually impossible to remove fine dust. However, it is best to predict the concentration of fine dust and minimize exposure to it. In this study, we developed a mathematical model that can predict the concentration of fine dust using various information related to the weather and air quality, which is provided in real time in 'Air Korea (http://www.airkorea.or.kr/)' and 'Weather Data Open Portal (https://data.kma.go.kr/).' In the mathematical model, various domestic seasonal variables and atmospheric state variables are extracted by multiple regression analysis. The parameters that have significant influence on the fine dust concentration are extracted, and using ANN (Artificial Neural Network) and SVM (Support Vector Machine), which are machine learning techniques, we proposed a prediction model. The proposed model can verify its effectiveness by using past dust and weather big data.

• 셀메드
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• 제11권1호
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• pp.5.1-5.2
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• 2021

The aim of this article is to argue that BanLyeo music (companion music) is much better than medicine. A companion who shares thoughts or actions, or a metaphorical description of an object that is always close or carried. Isn't the music that we share in our daily lives a BanLyeo music (companion music)? Music stays with us forever as long as we choose. Therefore, it is music that can go with us until the end, so I think we should call it BanLyeo music (companion music). Music can be with us whenever and wherever we want, soothing sadness and pain and cheering us up. Here is a person who is living a second life happily because of BanLyeo music. Beyond the passive listening to music, direct and active music performance is a great power to save one person. As a more effective healing agent than medicine, BanLyeo music is a great power to stay together for the rest of your life and cheer you up. So, I think music is much better than medicine.

• Journal of Information Processing Systems
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• 제18권4호
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• pp.524-534
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• 2022

In this paper, we produce Trash Object Detection (TOD) dataset to solve trash detection problems. A well-organized dataset of sufficient size is essential to train object detection models and apply them to specific tasks. However, existing trash datasets have only a few hundred images, which are not sufficient to train deep neural networks. Most datasets are classification datasets that simply classify categories without location information. In addition, existing datasets differ from the actual guidelines for separating and discharging recyclables because the category definition is primarily the shape of the object. To address these issues, we build and experiment with trash datasets larger than conventional trash datasets and have more than twice the resolution. It was intended for general household goods. And annotated based on guidelines for separating and discharging recyclables from the Ministry of Environment. Our dataset has 10 categories, and around 33K objects were annotated for around 5K images with 1280×720 resolution. The dataset, as well as the pre-trained models, have been released at https://github.com/jms0923/tod.