• Journal of the Korean Solar Energy Society
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• v.27 no.4
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• pp.147-155
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• 2007

It is essential to investigate the structure and the main characteristic of Home USN (Ubiquitous Sensor Network) technologies in built ubiquitous environment while designing future residential space. For this study, three different housing types have been selected to implement ubiquitous technologies for residential space; those are regular, elderly, and single residence units. It is certain that efficiency of ubiquitous home design is improved if main components of each specific housing type are analyzed precisely in digital way and design models are prepared accordingly. Ubiquitous technology, in conclusion, has to be applied not on)r with systematical mechanism or electronic setting but in human-centered atmosphere as well, keeping with deep consideration for bio-housing service factors in eco-friendly surrounding; we call this Ubiquitous Humanism.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.64.3-64.3
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• 2017

For the detection of transient sources in optical wide field surveys like KMTNet Supernova Project, difference imaging technique is commonly used. As this method produces a fair amount of false positives, it is also common to utilize machine learning algorithms to screen likely true positives. While deep learning methods such as a convolutional neural network has been successfully applied recently, its application can be limited if the size of the training sample is small. I will discuss a variation of more conventional method that adopts the wavelet transform for feature engineering and its performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1452-1455
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• 2021

Recently, point clouds are generated by capturing real space in 3D, and it is actively applied and serviced for performances, exhibitions, education, and training. These point cloud data require post-correction work to be used in virtual environments due to errors caused by the capture environment with sensors and cameras. In this paper, we propose an enhancement technique for 3D point cloud data by applying generative adversarial network(GAN). Thus, we performed an approach to regenerate point clouds as an input of GAN. Through our method presented in this paper, point clouds with a lot of noise is configured in the same shape as the real object and environment, enabling precise interaction with the reconstructed content.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.227-237
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• 2022

The most critical factors for detecting changes in very high-resolution satellite images are building positional inconsistencies and relief displacements caused by satellite side-view. To resolve the above problems, additional processing using a digital elevation model and deep learning approach have been proposed. Unfortunately, these approaches are not sufficiently effective in solving these problems. This study proposed a change detection method that considers both positional and topology information of buildings. Mask R-CNN (Region-based Convolutional Neural Network) was trained on a SpaceNet building detection v2 dataset, and the central points of each building were extracted as building nodes. Then, triangulated irregular network graphs were created on building nodes from temporal images. To extract the area, where there is a structural difference between two graphs, a change index reflecting the similarity of the graphs and differences in the location of building nodes was proposed. Finally, newly changed or deleted buildings were detected by comparing the two graphs. Three pairs of test sites were selected to evaluate the proposed method's effectiveness, and the results showed that changed buildings were detected in the case of side-view satellite images with building positional inconsistencies.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.60.2-60.2
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• 2020

In the hierarchical framework, galaxies grow through mergers and accretion. Those mechanisms leave faint features, such as stellar streams, shells and smooth stellar halos in the outskirts of galaxies. In order to search for those features in the nearby galaxies, we are conducting a KMTNet Nearby Galaxy Survey using the Korea Microlensing Telescope Network. We present a deep and wide-field imaging of NGC 5236, a barred spiral galaxy. In one-dimensional surface brightness profiles, we reach 28, 29 mag/arcsec2 in the R- and B-band, respectively. We find that the outer disk of NGC 5236 can be well described with a single exponential profile up to 17 kpc (~3.8 Reff) indicating that the excess light due to the stellar halo is not clearly detected. B-R color gradually increases towards the outskirts of the galaxy. It may reveal that stellar properties in the outskirts are marginally distinctive from those in the central part.

• Journal of Astronomy and Space Sciences
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• v.40 no.3
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• pp.123-129
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• 2023

This paper presents an analysis of the trans-lunar trajectory insertion performance of the Korea Pathfinder Lunar Orbiter (KPLO), the first lunar exploration spacecraft of the Republic of Korea. The successful launch conducted on August 4, 2022 (UTC), utilized the SpaceX Falcon 9 rocket from Cape Canaveral Space Force Station. The trans-lunar trajectory insertion performance plays a crucial role in ensuring the overall mission success by directly influencing the spacecraft's onboard fuel consumption. Following separation from the launch vehicle (LV), a comprehensive analysis of the trajectory insertion performance was performed by the KPLO flight dynamics (FD) team. Both orbit parameter message (OPM) and orbit determination (OD) solutions were employed using deep space network (DSN) tracking measurements. As a result, the KPLO was accurately inserted into the ballistic lunar transfer (BLT) trajectory, satisfying all separation requirements at the target interface point (TIP), including launch injection energy per unit mass (C3), right ascension of the injection orbit apoapsis vector (RAV), and declination of the injection orbit apoapsis vector (DAV). The precise BLT trajectory insertion facilitated the smoother operation of the KPLO's remainder mission phase and enabled the utilization of reserved fuel, consequently significantly enhancing the possibilities of an extended mission.

• Tunnel and Underground Space
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• v.30 no.5
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• pp.462-472
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• 2020

Rock classification is fundamental discipline of exploring geological and geotechnical features in a site, which, however, may not be easy works because of high diversity of rock shape and color according to its origin, geological history and so on. With the great success of convolutional neural networks (CNN) in many different image-based classification tasks, there has been increasing interest in taking advantage of CNN to classify geological material. In this study, a feasibility of the deep CNN is investigated for automatically and accurately identifying rock types, focusing on the condition of various shapes and colors even in the same rock type. It can be further developed to a mobile application for assisting geologist in classifying rocks in fieldwork. The structure of CNN model used in this study is based on a deep residual neural network (ResNet), which is an ultra-deep CNN using in object detection and classification. The proposed CNN was trained on 10 typical rock types with an overall accuracy of 84% on the test set. The result demonstrates that the proposed approach is not only able to classify rock type using images, but also represents an improvement as taking highly diverse rock image dataset as input.

• Journal of Satellite, Information and Communications
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• v.11 no.3
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• pp.86-91
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• 2016

KARI(Korea Aerospace Research Institute) has been developing the KPLO(Korea Pathfinder Lunar Orbiter) for Korean first lunar exploration, and analysing various subjects for the mission success. Especially the performance of the communication is one of important factors, because massive scientific and technical data acquired by multiple payloads might be transferred to ground stations on the Earth. In this paper, we explained the study on the 1-day average downlink capacity based on the visibility analysis between ground stations and KPLO, and described its results.

• Journal of Biomedical Engineering Research
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• v.42 no.5
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• pp.232-240
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• 2021

Heterogeneity in cancer is the major obstacle for precision medicine and has become a critical issue in the field of a cancer diagnosis. Many attempts were made to disentangle the complexity by molecular classification. However, multi-dimensional information from dynamic responses of cancer poses fundamental limitations on biomolecular marker-based conventional approaches. Cell morphology, which reflects the physiological state of the cell, can be used to track the temporal behavior of cancer cells conveniently. Here, we first present a hybrid learning-based platform that extracts cell morphology in a time-dependent manner using a deep convolutional neural network to incorporate multivariate data. Feature selection from more than 200 morphological features is conducted, which filters out less significant variables to enhance interpretation. Our platform then performs unsupervised clustering to unveil dynamic behavior patterns hidden from a high-dimensional dataset. As a result, we visualize morphology state-space by two-dimensional embedding as well as representative morphology clusters and trajectories. This cell morphology profiling strategy by hybrid learning enables simplification of the heterogeneous population of cancer.

• Convergence Security Journal
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• v.23 no.4
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• pp.61-70
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• 2023

Clustering is an unsupervised learning method that involves grouping data based on features such as distance metrics, using data without known labels or ground truth values. This method has the advantage of being applicable to various types of data, including images, text, and audio, without the need for labeling. Traditional clustering techniques involve applying dimensionality reduction methods or extracting specific features to perform clustering. However, with the advancement of deep learning models, research on deep clustering techniques using techniques such as autoencoders and generative adversarial networks, which represent input data as latent vectors, has emerged. In this study, we propose a deep clustering technique based on deep learning. In this approach, we use an autoencoder to transform the input data into latent vectors, and then construct a vector space according to the cluster structure and perform k-means clustering. We conducted experiments using the MNIST and Fashion-MNIST datasets in the PyTorch machine learning library as the experimental environment. The model used is a convolutional neural network-based autoencoder model. The experimental results show an accuracy of 89.42% for MNIST and 56.64% for Fashion-MNIST when k is set to 10.