• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.12
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• pp.14-21
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• 2013

In satellite operations and space exploration missions, a ranging is one of the most essential technologies to get its navigational information of space probes. Recently, the importance of cross-support between space agencies is increasing for more fine performance of space mission. For cross-support, mutually compatible ranging system between space agencies is recommended. For these reasons, the consultative committee for space data systems (CCSDS) recommends pseudo noise (PN) ranging as a digital standard ranging system. The length of PN sequence in CCSDS standard is proper for deep space missions, however, it is too long to use for ranging in near earth missions. In this paper, we propose Variable Length PN sequence schemes suitable for ranging of near earth satellites, such as low-earth orbit (LEO), medium-earth orbit (MEO) and Geostationary orbit (GEO). Therefore we propose variable length PN sequence ranging system including CCSDS standard for multiple missions.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.207-221
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• 2023

This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.819-834
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• 2022

Artificial Intelligence (AI) techniques have been effectively used for image classification, object detection, and image segmentation. Along with the recent advancement of computing power, deep learning models can build deeper and thicker networks and achieve better performance by creating more appropriate feature maps based on effective activation functions and optimizer algorithms. This review paper examined technical and academic trends of Convolutional Neural Network (CNN) and Transformer models that are emerging techniques in remote sensing and suggested their utilization strategies and development directions. A timely supply of satellite images and real-time processing for deep learning to cope with disaster monitoring will be required for future work. In addition, a big data platform dedicated to satellite images should be developed and integrated with drone and Closed-circuit Television (CCTV) images.

• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.125-131
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• 2019

A vanishing point is a point on an image to which parallel lines projected from a real space gather. A vanishing point in a road space provides important spatial information. It is possible to improve the position of an extracted lane or generate a depth map image using a vanishing point in the road space. In this paper, we propose a method of detecting vanishing points on images taken from a vehicle's point of view using Deep Neural Network (DNN) and Histogram of Oriented Gradient (HoG). The proposed algorithm is divided into a HoG feature extraction step, in which the edge direction is extracted by dividing an image into blocks, a DNN learning step, and a test step. In the learning stage, learning is performed using 2,300 road images taken from a vehicle's point of views. In the test phase, the efficiency of the proposed algorithm using the Normalized Euclidean Distance (NormDist) method is measured.

• Investigative Magnetic Resonance Imaging
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• v.25 no.4
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• pp.300-312
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• 2021

Compressed sensing (CS) has been investigated in magnetic resonance (MR) parametric mapping to reduce scan time. However, the relatively long reconstruction time restricts its widespread applications in the clinic. Recently, deep learning-based methods have shown great potential in accelerating reconstruction time and improving imaging quality in fast MR imaging, although their adaptation to parametric mapping is still in an early stage. In this paper, we proposed a novel deep learning-based framework DEMO for fast and robust MR parametric mapping. Different from current deep learning-based methods, DEMO trains the network in an unsupervised way, which is more practical given that it is difficult to acquire large fully sampled training data of parametric-weighted images. Specifically, a CS-based loss function is used in DEMO to avoid the necessity of using fully sampled k-space data as the label, thus making it an unsupervised learning approach. DEMO reconstructs parametric weighted images and generates a parametric map simultaneously by unrolling an interaction approach in conventional fast MR parametric mapping, which enables multi-tasking learning. Experimental results showed promising performance of the proposed DEMO framework in quantitative MR T1ρ mapping.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.61.1-61.1
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• 2017

As the application of deep-learning methods has been succeeded in various fields, they have a high potential to be applied to space weather forecasting. Convolutional neural network, one of deep learning methods, is specialized in image recognition. In this study, we apply the AlexNet architecture, which is a winner of Imagenet Large Scale Virtual Recognition Challenge (ILSVRC) 2012, to the forecast of daily solar flare occurrence using the MatConvNet software of MATLAB. Our input images are SOHO/MDI, EIT $195{\AA}$, and $304{\AA}$ from January 1996 to December 2010, and output ones are yes or no of flare occurrence. We consider other input images which consist of last two images and their difference image. We select training dataset from Jan 1996 to Dec 2000 and from Jan 2003 to Dec 2008. Testing dataset is chosen from Jan 2001 to Dec 2002 and from Jan 2009 to Dec 2010 in order to consider the solar cycle effect. In training dataset, we randomly select one fifth of training data for validation dataset to avoid the over-fitting problem. Our model successfully forecasts the flare occurrence with about 0.90 probability of detection (POD) for common flares (C-, M-, and X-class). While POD of major flares (M- and X-class) forecasting is 0.96, false alarm rate (FAR) also scores relatively high(0.60). We also present several statistical parameters such as critical success index (CSI) and true skill statistics (TSS). All statistical parameters do not strongly depend on the number of input data sets. Our model can immediately be applied to automatic forecasting service when image data are available.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.2
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• pp.122.2-122.2
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• 2011

For more than a decade, NEA (Near-Earth Asteroid) survey teams equipped with 1 meter-class telescopes discovered thousands of NEAs in the northern sky. As of August 2011, some 8,200 NEAs have been cataloged, yet only five percent of them has been investigated for their physical and chemical properties. In order to improve current situation, we propose a deep ecliptic survey utilizing KMTNet, for detection and characterization of NEAs in the southern sky. Thanks to the wide-field capability (four square degrees) of the telescopes, we will be able to considerably expand the search volume carrying out precision photometry down to 21.5th magnitude. We plan to focus our survey on opposition and two "sweet spots" in the ecliptic belt. Since SDSS colors characterize mineralogical properties of NEAs, g', r', i', z' filters will be employed. Based on the round-the-clock observation, we will study their rotational properties; for multiple systems, mass, density and other physical parameters can be obtained. We plan to maintain a dedicated database of the physical and mineralogical properties of NEAs. With this archive, it is expected that our understanding on the population will see a drastic change. We also plan to participate in the GAIA Follow-Up Network for ground based observation of the Solar System Objects (GAIA-FUN-SSO). The follow- up astrometry will be performed upon alerts issued by the GAIA-FUN-SSO Central Node in France.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.559-570
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• 2022

When a satellite image has low spatial resolution, it is difficult to detect small objects. In this research, we aim to check the effect of super resolution on object detection. Super resolution is a software method that increases the resolution of an image. Unpaired super resolution network is used to improve Sentinel-2's spatial resolution from 10 m to 3.2 m. Faster-RCNN, RetinaNet, FCOS, and S²ANet were used to detect vessels in the Sentinel-2 images. We experimented the change in vessel detection performance when super resolution is applied. As a result, the Average Precision (AP) improved by at least 12.3% and up to 33.3% in the ship detection models trained with the super-resolution image. False positive and false negative cases also decreased. This implies that super resolution can be an important pre-processing step in object detection, and it is expected to greatly contribute to improving the accuracy of other image-based deep learning technologies along with object detection.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.4
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• pp.212-214
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• 2014

In this paper, we present the scenario designs of the possibility of space communications for Korean Lunar Explorer and the analysis of its performance, depending upon the explorer's position within the moon's orbit after being launched from earth. As per each scenario, we would like to propose the analysis of the possible communication times and total transmission throughput data per day in two cases: one for using DSN and another for using only Korean's ground station.

• 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.