• The Bulletin of The Korean Astronomical Society
• /
• v.46 no.1
• /
• pp.42.2-43
• /
• 2021

We address a question of what are three main channels that can best translate other channels in ultraviolet (UV) and extreme UV (EUV) observations. For this, we compare the image translations among the nine channels of the Atmospheric Imaging Assembly on the Solar Dynamics Observatory using a deep learning model based on conditional generative adversarial networks. In this study, we develop 170 deep learning models: 72 models for single-channel input, 56 models for double-channel input, and 42 models for triple-channel input. All models have a single-channel output. Then we evaluate the model results by pixel-to-pixel correlation coefficients (CCs) within the solar disk. Major results from this study are as follows. First, the model with 131 Å shows the best performance (average CC = 0.84) among single-channel models. Second, the model with 131 and 1600 Å shows the best translation (average CC = 0.95) among double-channel models. Third, among the triple-channel models with the highest average CC (0.97), the model with 131, 1600, and 304 Å is suggested in that the minimum CC (0.96) is the highest. Interestingly they are representative coronal, photospheric, and chromospheric lines, respectively. Our results may be used as a secondary perspective in addition to primary scientific purposes in selecting a few channels of an UV/EUV imaging instrument for future solar satellite missions.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.38 no.1
• /
• pp.23-33
• /
• 2020

Recently, high-resolution images can be easily acquired using UAV (Unmanned Aerial Vehicle), so that it is possible to produce small area observation and spatial information at low cost. In particular, research on the generation of cover maps in crop production areas is being actively conducted for monitoring the agricultural environment. As a result of comparing classification performance by applying RF(Random Forest), SVM(Support Vector Machine) and CNN(Convolutional Neural Network), deep learning classification method has many advantages in image classification. In particular, land cover classification using satellite images has the advantage of accuracy and time of classification using satellite image data set and pre-trained parameters. However, UAV images have different characteristics such as satellite images and spatial resolution, which makes it difficult to apply them. In order to solve this problem, we conducted a study on the application of deep learning algorithms that can be used for analyzing agricultural lands where UAV data sets and small-scale composite cover exist in Korea. In this study, we applied DeepLab V3 +, FC-DenseNet (Fully Convolutional DenseNets) and FRRN-B (Full-Resolution Residual Networks), the semantic image classification of the state-of-art algorithm, to UAV data set. As a result, DeepLab V3 + and FC-DenseNet have an overall accuracy of 97% and a Kappa coefficient of 0.92, which is higher than the conventional classification. The applicability of the cover classification using UAV images of small areas is shown.

• Journal of Korea Water Resources Association
• /
• v.45 no.7
• /
• pp.631-641
• /
• 2012

In this study, satellite data (MTSAT-1R), a numerical weather prediction model, RDAPS (Regional Data Assimilation and Prediction System) output, ground weather station data, and artificial neural networks were used to improve the accuracy of summer rainfall forecasts. The developed model was applied to the Seoul station to forecast the rainfall at 3, 6, 9, and 12-hour lead times. Also to reflect the different weather conditions during the summer season which is related to the frontal precipitation and the cyclonic precipitation such as Jangma and Typhoon, the neural network models were formed for two different periods of June-July and August-September respectively. The rainfall forecast model was trained during the summer season of 2006 and 2008 and was verified for that of 2009 based on the data availability. The results demonstrated that the model allows us to get the improved rainfall forecasts until lead time of 6 hour, but there is still a large room to improve the rainfall forecast skill.

• Korean Journal of Remote Sensing
• /
• v.37 no.5_3
• /
• pp.1405-1423
• /
• 2021

Precipitation is one of the main factors that affect water and energy cycles, and its estimation plays a very important role in securing water resources and timely responding to water disasters. Satellite-based quantitative precipitation estimation (QPE) has the advantage of covering large areas at high spatiotemporal resolution. In this study, machine learning-based rainfall intensity models were developed using Himawari-8 Advanced Himawari Imager (AHI) water vapor channel (6.7 ㎛), infrared channel (10.8 ㎛), and weather radar Column Max (CMAX) composite data based on random forest (RF). The target variables were weather radar reflectivity (dBZ) and rainfall intensity (mm/hr) converted by the Z-R relationship. The results showed that the model which learned CMAX reflectivity produced the Critical Success Index (CSI) of 0.34 and the Mean-Absolute-Error (MAE) of 4.82 mm/hr. When compared to the GeoKompsat-2 and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS) rainfall intensity products, the accuracies improved by 21.73% and 10.81% for CSI, and 31.33% and 23.49% for MAE, respectively. The spatial distribution of the estimated rainfall intensity was much more similar to the radar data than the existing products.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.8
• /
• pp.3551-3567
• /
• 2016

The Low Earth Orbit satellite network has the unique characteristics of the non-uniform and time-variant traffic load distribution, which often causes severe link congestion and thus results in poor performance for delay-sensitive flows, especially when the network is heavily loaded. To solve this problem, a novel adaptive routing algorithm, referred to as the delay-oriented adaptive routing algorithm (DOAR), is proposed. Different from current reactive schemes, DOAR employs Destination-Sequenced Distance-Vector (DSDV) routing algorithm, which is a proactive scheme. DSDV is extended to a multipath QoS version to generate alternative routes in active with real-time delay metric, which leads to two significant advantages. First, the flows can be timely and accurately detected for route adjustment. Second, it enables fast, flexible, and optimized QoS matching between the alternative routes and adjustment requiring flows and meanwhile avoids delay growth caused by increased hop number and diffused congestion range. In addition, a retrospective route adjustment requesting scheme is designed in DOAR to enlarge the alternative routes set in the severe congestion state in a large area. Simulation result suggests that DOAR performs better than typical adaptive routing algorithms in terms of the throughput and the delay in a variety of traffic intensity.

• Journal of Astronomy and Space Sciences
• /
• v.24 no.4
• /
• pp.417-430
• /
• 2007

The selection of ground stations is one of the essential process of IGS (International GNSS Service) products. High quality GPS data should be collected from the globally distributed ground stations. In this study, we investigated an effect of ground station network selection on GPS satellite ephemeris. The GPS satellite ephemeris obtained from the twelve ground station networks were analyzed to investigate the effect of selection of ground stations. For data quality check, the observations, the number of cycle slips, and multipath of pseudoranges for L1 and L2 were considered. The ideal network defined by Taylor-Karman structure and SOD (Second Order Design) were used to obtain the optimal ground station network.

• Journal of Communications and Networks
• /
• v.12 no.6
• /
• pp.568-576
• /
• 2010

The paper presents a control architecture aimed at implementing bandwidth optimization combined with call admission control (CAC) over a digital video broadcasting (DVB) return channel satellite terminal (RCST) under quality of service (QoS) constraints. The approach can be applied in all cases where traffic flows, coming from a terrestrial portion of the network, are merged together within a single DVB flow, which is then forwarded over the satellite channel. The paper introduces the architecture of data and control plane of the RCST at layer 2. The data plane is composed of a set of traffic buffers served with a given bandwidth. The control plane proposed in this paper includes a layer 2 resource manager (L2RM), which is structured into decision makers (DM), one for each traffic buffer of the data plane. Each DM contains a virtual queue, which exactly duplicates the corresponding traffic buffer and performs the actions to compute the minimum bandwidth need to assure the QoS constraints. After computing the minimum bandwidth through a given algorithm (in this view the paper reports some schemes taken in the literature which may be applied), each DM communicates this bandwidth value to the L2RM, which allocates bandwidth to traffic buffers at the data plane. Real bandwidth allocations are driven by the information provided by the DMs. Bandwidth control is linked to a CAC scheme, which uses current bandwidth allocations and peak bandwidth of the call entering the network to decide admission. The performance evaluation is dedicated to show the efficiency of the proposed combined bandwidth allocation and CAC.

• Journal of the Society of Disaster Information
• /
• v.1 no.1
• /
• pp.91-119
• /
• 2005

When the national disaster accident is occurred, it is difficult to maintain the mutual cooperation systems. In order to solve the problems, the construction of the national unified command wireless network is necessary. In this paper, the specified state of the characteristic frequency of the digital TRS wireless network constructed recently is investigated and analyzed. Through the analysis, the problems of the construction of the national unified command wireless network are grasped. To solve the problem, it is proposed that the digital TRS wireless network is connected with the satellite communication network, and connected with the existing wireless network, LMR. In the concretely it is proposed that the natural unified wireless network should be proceeded step by step. At first, for 2 years the existing networks of the Fire Fighting Agency, the Police, the Forest Service and so on must be utilized and prepared to link with TRS. The second, for 2 years it is carried forward a scheme to maintain the properties of the agencies concerned. Further, it must be prepared to connect with satellite network. At third, for 2 years all agencies concerned with the fire fighting and the disaster prevention must be unified, and the systems have to be promoted for the p1an of linkage of TRS network and the existing network. Next the agencies concerned have to be unified and the authority has to be intensified. When a disaster is occurred, the National Emergency Management Agency has to play a central role. In a local area it has to be given the Fire Fighting Agency an authority and a duty to get ready for each emergency situation.

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.9
• /
• pp.2460-2469
• /
• 1999

Recently, appearing of various wireless telecommunications services, the competitive power of paging service is requested. In this paper, two-way paging earth station for satellite was proposed and designed to enhance the competitive power of cost and technique, and the link budget of this two-way paging networks using Koreas at was discussed. To reduce the cost of remote station, the antenna and HPA size of Hub and remote station were differently designed, and an idea of power allocations of inbound and outbound carriers was proposed and utilized in this link budget of two-way earth station for using satellite. The results of this link budget were follows. At the 0.043% time rate of rain, antenna size of Hub and remote station were 3.7m and 1.2m, the HPA size of Hub and remote station were 10.37W and 2.0W, and power allocations of outbound and inbound were 84% and 16% respectively. At the 0.02% time rate of rain, the antenna size of Hub and remote station were 3.7m and 1.8m and power allocations of outbound and inbound were 63% and 37% respectively.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.173-173
• /
• 2023

The Mekong River Basin (MRB) is a crucial watershed in Asia, impacting over 60 million people across six developing nations. Accurate satellite-based precipitation products (SPPs) are essential for effective hydrological and watershed management in this region. However, the performance of SPPs has been varied and limited. The APHRODITE product, a unique gauge-based dataset for MRB, is widely used but is only available until 2015. In this study, we present a novel framework for correcting SPPs in the MRB by employing a deep learning approach that combines convolutional neural networks and encoder-decoder architecture to address pixel-by-pixel bias and enhance accuracy. The DLF was applied to four widely used SPPs (TRMM, CMORPH, CHIRPS, and PERSIANN-CDR) in MRB. For the original SPPs, the TRMM product outperformed the other SPPs. Results revealed that the DLF effectively bridged the spatial-temporal gap between the SPPs and the gauge-based dataset (APHRODITE). Among the four corrected products, ADJ-TRMM demonstrated the best performance, followed by ADJ-CDR, ADJ-CHIRPS, and ADJ-CMORPH. The DLF offered a robust and adaptable solution for bias correction in the MRB and beyond, capable of detecting intricate patterns and learning from data to make appropriate adjustments. With the discontinuation of the APHRODITE product, DLF represents a promising solution for generating a more current and reliable dataset for MRB research. This research showcased the potential of deep learning-based methods for improving the accuracy of SPPs, particularly in regions like the MRB, where gauge-based datasets are limited or discontinued.