• Electronics and Telecommunications Trends
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• v.38 no.2
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• pp.1-11
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• 2023

The recent explosion in the number of low earth orbit (LEO) satellites launched to space allows to easily anticipate that the number of satellites in orbit will sustain a dramatic increase. As satellite components are integrated and unified with terrestrial cellular networks, they will play a key role in providing coverage and resilience for future cellular networks. We provide a brief overview of typical scenarios and network architectures for cellular-based LEO satellite communication systems. In addition, we outline 3GPP standardization trends in non-terrestrial networks and satellite access based on 5G/5G Advanced systems and analyze future evolution prospects of cellular-based satellite communication systems.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.53.4-53.4
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• 2019

We reconstruct the underlying dark matter (DM) density distribution of the local universe within 20Mpc/h cubic box by using the galaxy position and peculiar velocity. About 1,000 subboxes in the Illustris-TNG cosmological simulation are used to train the relation between DM density distribution and galaxy properties by using UNet-like convolutional neural network (CNN). The estimated DM density distributions have a good agreement with their truth values in terms of pixel-to-pixel correlation, the probability distribution of DM density, and matter power spectrum. We apply the trained CNN architecture to the galaxy properties from the Cosmicflows-3 catalogue to reconstruct the DM density distribution of the local universe. The reconstructed DM density distribution can be used to understand the evolution and fate of our local environment.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.53.3-53.3
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• 2019

Recent work by Ravanbakhsh et al. (2017), Mathuriya et al. (2018) showed that convolutional neural networks (CNN) can be trained to predict cosmological parameters from the visual shape of the large scale structure, i.e. the filaments, clusters and voids of the cosmic density field. These preliminary works used the dark matter density field at redshift zero. We build upon these works by considering realistic mock galaxy catalogues that mimic true observations. We construct light-cones that span the redshift range appropriate for current and near future cosmological surveys such as LSST, EUCLID, WFIRST etc. In summary, we propose a novel multi-image input CNN to track the evolution in the morphology of large scale structures over cosmic time to constrain cosmology and the expansion history of the Universe.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.78.3-78.3
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• 2019

We develop a novel technique that can constrain the evolutionary track of the epoch of reionization (EoR) by applying the convolutional neural network (CNN) to the 21-cm differential brightness temperature. We use 21cmFAST, a fast semi-numerical cosmological 21-cm signal simulator, to produce mock 21-cm map between z=6-13. We design a CNN architecture that predicts the volume-averaged neutral hydrogen fraction from the given 21-cm map. The estimated neutral fraction has a good agreement with its truth value even after smoothing the 21-cm map with somewhat realistic choices of beam size and the frequency bandwidth of the Square Kilometre Array (SKA). Our technique could be further utilized to denoise the 21-cm map or constrain the properties of the radiation sources.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.71.2-71.2
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• 2019

In order to gather clues to surface mineralogy of asteroids, we classify their taxonomy based on their reflected spectra. It is remarkable that a large number of asteroids plotted in the proper orbital element space with distinct colors according to their taxonomic types reveal the dynamical evolution and the structure in the near-Earth space, the main-belt and beyond. Although we have ~1×10⁶ known objects, no more than ~3×10³ of them are properly classified taxonomically as visible-near infrared spectroscopy is costly. On the other hand, multi-wavelength broadband photometry in the visible region provides a rather inexpensive alternative tool for approximate taxonomy. Thus we have conducted multi-band observations systematically using Korea Microlensing Telescope Network (KMTNet) with BVRI and griz filters since back in 2015. We then applied aperture photometry with elliptical apertures to fit the trails of objects during the exposures, and classified them with the principle component indices of Ivezic et al. (2001). We will make use of our new, three dimensional asteroid classification scheme for the next step.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.40-42
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• 2022

People's focus steadily shifted toward fashion as a popular aesthetic expression as their quality of life improved. Humans are inevitably drawn to things that are more aesthetically appealing. This human proclivity has resulted in the evolution of the fashion industry over time. However, too many clothing alternatives on e-commerce platforms have created additional obstacles for clients in recognizing their suitable outfit. Thus, in this paper, we proposed a personalized Fashion Recommender system that generates recommendations for the user based on their previous purchases and history. Our model aims to generate recommendations using an image of a product given as input by the user because many times people find something that they are interested in and tend to look for products that are like that. In the system, we first reduce data dimensionality by component analysis to avoid the curse of dimensionality, and then the final suggestion is generated by neural network. To create the final suggestions, we have employed neural networks to evaluate photos from the H&M dataset and a nearest neighbor backed recommender.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.27.4-28
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• 2021

Since the first direct detection of the gravitational waves in 2015, more than 50 events coming from the merging of compact binaries composed of black holes and neutron stars have been observed. The simultaneous detection of gravitational waves and electromagnetics waves from the merging of neutron stars opened up multi-messenger astronomy. The forthcoming observations with better sensitivity by the network of ground based detectors will enrich the gravitational wave source populations and provide valuable information regarding stellar evolution, dynamics of dense stellar systems, and star formation history across the cosmic time. The precision of the Hubble constant from the distance measurement of gravitational sources will improve with more binary neutron star events are observed together with the aftweglows. I will also briefly cover the expected scientiic outcomes from the future detectors that are sensitive to much lower frequenies than current detectors.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.439-444
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• 2011

As construction projects have been getting larger and more complex, a single individual or organization cannot have complete knowledge or the abilities to handle all matters. Collaborative practices among heterogeneous individuals, which are temporarily congregated to carry out a project, are required in order to accomplish project objectives. These organizational knowledge creation processes of project teams should be understood from the active and dynamic viewpoint of how they create information and knowledge rather than from the passive and static input-process-output sequence. To this end, agent-based modeling and simulation which is built from the ground-up perspective can provide the most appropriate way to systematically investigate them. In this paper, agent-based modeling and simulation as a research method and a medium for representing theory is introduced. To illustrate, an agent-based simulation of the evolution of collaboration in large-scale project teams from a game theory and social network perspective is presented.

• Electronics and Telecommunications Trends
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• v.39 no.2
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• pp.43-53
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• 2024

The media environment is rapidly evolving to be tailored to viewers using personal mobile devices in accordance with technological evolution and changes in social structures. Broadcast media technology is also advancing to enable new services, including data casting, in various reception environments beyond the existing fixed environment and one-way audio/video content services. In addition, technologies to increase the transmission capacity to accommodate next-generation large-capacity media content as well as communication network utilization and convergence technologies are being developed to facilitate interactive services and expand the broadcasting coverage. We discuss the current status and future prospects in broadcasting technology for terrestrial and mobile communication systems and analyze broadcasting technology elements for upcoming media environments relying on generative artificial intelligence.

• Geomechanics and Engineering
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• v.37 no.3
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• pp.263-277
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• 2024

Artificial Neural Networks (ANNs) are artificial learning algorithms that provide successful results in solving many machine learning problems such as classification, prediction, object detection, object segmentation, image and video classification. There is an increasing number of studies that use ANNs as a prediction tool in soil classification. The aim of this research was to understand the role of hyperparameter optimization in enhancing the accuracy of ANNs for soil type classification. The research results has shown that the hyperparameter optimization and hyperparamter optimized ANNs can be utilized as an efficient mechanism for increasing the estimation accuracy for this problem. It is observed that the developed hyperparameter tool (HyperNetExplorer) that is utilizing the Covariance Matrix Adaptation Evolution Strategy (CMAES), Genetic Algorithm (GA) and Jaya Algorithm (JA) optimization techniques can be successfully used for the discovery of hyperparameter optimized ANNs, which can accomplish soil classification with 100% accuracy.