• Tunnel and Underground Space
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• v.26 no.1
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• pp.32-45
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• 2016

For effective utilization of downtown area, many studies about underground have been performed around the world, and double-deck tunnel have being operated in USA, Europe and China, etc. (A86 East Duplex in France, M30 tunnel project in Spain, SR-99 in seattle, USA, Yangtze river tunnel in China) In Korea, the research about network type double-deck tunnel in deep underground space is in progress to solve the traffic jam and secure the ground space. In this study, a number of factors required for double-deck tunnel in deep underground are analyzed through the existing ventilation design outline and unique ventilation design factors for network type double-deck tunnel are established by reviewing design cases of overseas double-deck tunnel.

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.4
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• pp.19-28
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• 2020

The Urban Heat Island (UHI) Effect has intensified due to urbanization and heat management at the urban level is treated as an important issue. Green space improvement projects and environmental policies are being implemented as a way to alleviate Urban Heat Islands. Several studies have been conducted to analyze the correlation between urban green areas and heat with linear regression models. However, linear regression models have limitations explaining the correlation between heat and the multitude of variables as heat is a result of a combination of non-linear factors. This study evaluated the Heat Island alleviating effects in Seoul during the summer by using a deep neural network model methodology, which has strengths in areas where it is difficult to analyze data with existing statistical analysis methods due to variable factors and a large amount of data. Wide-area data was acquired using Landsat 8. Seoul was divided into a grid (30m × 30m) and the heat island reduction variables were enter in each grid space to create a data structure that is needed for the construction of a deep neural network using ArcGIS 10.7 and Python3.7 with Keras. This deep neural network was used to analyze the correlation between land surface temperature and the variables. We confirmed that the deep neural network model has high explanatory accuracy. It was found that the cooling effect by NDVI was the greatest, and cooling effects due to the park size and green space proximity were also shown. Previous studies showed that the cooling effects related to park size was 2℃-3℃, and the proximity effect was found to lower the temperature 0.3℃-2.3℃. There is a possibility of overestimation of the results of previous studies. The results of this study can provide objective information for the justification and more effective formation of new urban green areas to alleviate the Urban Heat Island phenomenon in the future.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.99-109
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• 2023

The darknet is frequently referred to as the hub of illicit online activity. In order to keep track of real-time applications and activities taking place on Darknet, traffic on that network must be analysed. It is without a doubt important to recognise network traffic tied to an unused Internet address in order to spot and investigate malicious online activity. Any observed network traffic is the result of mis-configuration from faked source addresses and another methods that monitor the unused space address because there are no genuine devices or hosts in an unused address block. Digital systems can now detect and identify darknet activity on their own thanks to recent advances in artificial intelligence. In this paper, offer a generalised method for deep learning-based detection and classification of darknet traffic. Furthermore, analyse a cutting-edge complicated dataset that contains a lot of information about darknet traffic. Next, examine various feature selection strategies to choose a best attribute for detecting and classifying darknet traffic. For the purpose of identifying threats using network properties acquired from darknet traffic, devised a hybrid deep learning (DL) approach that combines Recurrent Neural Network (RNN) and Bidirectional LSTM (BiLSTM). This probing technique can tell malicious traffic from legitimate traffic. The results show that the suggested strategy works better than the existing ways by producing the highest level of accuracy for categorising darknet traffic using the Black widow optimization algorithm as a feature selection approach and RNN-BiLSTM as a recognition model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.3D
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• pp.283-289
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• 2012

Because railways themselves are not enough to provide door-to-door service, they need to be complemented by a transportation system access the stations and a convenient transfer system to access other railway lines. User friendly service policies include a faster, interconnected, and intermodal transportation system. It becomes more important to use deep subterranean space from an economical standpoint in order to promote railway construction projects. In this research, the authors dealt with construction plan for strengthening capacity of deep subterranean railways. Since deep subterranean railways are situated deeper underground than other railways, they are more difficult to access and transfer to, in addition to psychological pressure. Moreover, deep subterranean railways with high speed add access difficulties as the distance between stations increases. Therefore, the authors discuss not only systemization for uniting deep subterranean railways and other transportation facilities but also reinforcement strategies. The purpose of these strategies is so that deep subterranean railways provide mobility while established railways provide accessibility to overcome this problem.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.1
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• pp.13-24
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• 2021

Tunnel boring machine (TBM) is widely used for tunnel excavation in hard rock and soft ground. In the perspective of TBM-based tunneling, one of the main challenges is to drive the machine optimally according to varying geological conditions, which could significantly lead to saving highly expensive costs by reducing the total operation time. Generally, drilling investigations are conducted to survey the geological ground before the TBM tunneling. However, it is difficult to provide the precise ground information over the whole tunnel path to operators because it acquires insufficient samples around the path sparsely and irregularly. To overcome this issue, in this study, we proposed a geological type classification system using the TBM operating data recorded in a 5 s sampling rate. We first categorized the various geological conditions (here, we limit to granite) as three geological types (i.e., rock, soil, and mixed type). Then, we applied the preprocessing methods including outlier rejection, normalization, and extracting input features, etc. We adopted a deep neural network (DNN), which has 6 hidden layers, to classify the geological types based on TBM operating data. We evaluated the classification system using the 10-fold cross-validation. Average classification accuracy presents the 75.4% (here, the total number of data were 388,639 samples). Our experimental results still need to improve accuracy but show that geology information classification technique based on TBM operating data could be utilized in the real environment to complement the sparse ground information.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.1
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• pp.56.3-57
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• 2015

As of early 2015, more than 12,000 Near-Earth Objects (NEOs) have been catalogued by the Minor Planet Center, however their observational properties such as broadband colors and rotational periods are known only for a small fraction of the population. Thanks to time series observations with the KMTNet, orbits, optical sizes (and albedo), spin states and three dimensional shapes of asteroids and comets including NEOs will be systematically investigated and archived for the first time. Based on SDSS and BVRI colors, their approximate surface mineralogy will also be characterized. This so-called DEEP-South (Deep Ecliptic Patrol of the Southern Sky) project will provide a prompt solution to the demand from the scientific community to bridge the gaps in global sky coverage with a coordinated use of the network of ground-based telescopes in the southern hemisphere. We will soon finish implementing dedicated software subsystem consisted of automated observation scheduler and data pipeline for the sake of increased discovery rate, rapid follow-up, timely phase coverage, and efficient data analysis. We will give a brief introduction to test runs conducted at CTIO with the first KMTNet telescope in February and March 2015 and experimental data processing. Preliminary scientific results will also be presented.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.54.2-54.2
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• 2019

Removing noise which occurs inevitably when taking image data has been a big concern. There is a way to raise signal-to-noise ratio and it is regarded as the only way, image stacking. Image stacking is averaging or just adding all pixel values of multiple pictures taken of a specific area. Its performance and reliability are unquestioned, but its weaknesses are also evident. Object with fast proper motion can be vanished, and most of all, it takes too long time. So if we can handle single shot image well and achieve similar performance, we can overcome those weaknesses. Recent developments in deep learning have enabled things that were not possible with former algorithm-based programming. One of the things is generating data with more information from data with less information. As a part of that, we reproduced stacked image from single shot image using a kind of deep learning, conditional generative adversarial network (cGAN). r-band camcol2 south data were used from SDSS Stripe 82 data. From all fields, image data which is stacked with only 22 individual images and, as a pair of stacked image, single pass data which were included in all stacked image were used. All used fields are cut in $128{\times}128$ pixel size, so total number of image is 17930. 14234 pairs of all images were used for training cGAN and 3696 pairs were used for verify the result. As a result, RMS error of pixel values between generated data from the best condition and target data were $7.67{\times}10^{-4}$ compared to original input data, $1.24{\times}10^{-3}$. We also applied to a few test galaxy images and generated images were similar to stacked images qualitatively compared to other de-noising methods. In addition, with photometry, The number count of stacked-cGAN matched sources is larger than that of single pass-stacked one, especially for fainter objects. Also, magnitude completeness became better in fainter objects. With this work, it is possible to observe reliably 1 magnitude fainter object.

• Journal of Astronomy and Space Sciences
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• v.36 no.4
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• pp.293-306
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• 2019

In this study, the observational arc-length effect on orbit determination (OD) for the Korea Pathfinder Lunar Orbiter (KPLO) in the Earth-Moon Transfer phase was investigated. For the OD, we employed a sequential estimation using the extended Kalman filter and a fixed-point smoother. The mission periods, comprised between the perigee maneuvers (PM) and the lunar orbit insertion (LOI) maneuver in a 3.5 phasing loop of the KPLO, was the primary target. The total period was divided into three phases: launch-PM1, PM1-PM3, and PM3-LOI. The Doppler and range data obtained from three tracking stations [included in the deep space network (DSN) and Korea Deep Space Antenna (KDSA)] were utilized for the OD. Six arc-length cases (24 hrs, 48 hrs, 60 hrs, 3 days, 4 days, and 5 days) were considered for the arc-length effect investigation. In order to evaluate the OD accuracy, we analyzed the position uncertainties, the precision of orbit overlaps, and the position differences between true and estimated trajectories. The maximum performance of 3-day OD approach was observed in the case of stable flight dynamics operations and robust navigation capability. This study provides a guideline for the flight dynamics operations of the KPLO in the trans-lunar phase.

• Journal of Internet Computing and Services
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• v.19 no.3
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• pp.43-48
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• 2018

In this paper, we propose an indoor-space recognition using DNN and super-pixel. In order to recognize the indoor space from the image, segmentation process is required for dividing an image Super-pixel is performed algorithm which can be divided into appropriate sizes. In order to recognize each segment, features are extracted using a proposed method. Extracted features are learned using DNN, and each segment is recognized using the DNN model. Experimental results show the performance comparison between the proposed method and existing algorithms.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.81.3-81.3
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• 2019

In this study, we apply conditional Generative Adversarial Network, which is one of the deep learning method, to the image-to-image translation from solar magentograms to solar UV and EUV images. For this, we train a model using pairs of SDO/AIA 9 wavelength UV and EUV images and their corresponding SDO/HMI line-of-sight magnetograms from 2011 to 2017 except August and September each year. We evaluate the model by comparing pairs of SDO/AIA images and corresponding generated ones in August and September. Our results from this study are as follows. First, we successfully generate SDO/AIA like solar UV and EUV images from SDO/HMI magnetograms. Second, our model has pixel-to-pixel correlation coefficients (CC) higher than 0.8 except 171. Third, our model slightly underestimates the pixel values in the view of Relative Error (RE), but the values are quite small. Fourth, considering CC and RE together, 1600 and 1700 photospheric UV line images, which have quite similar structures to the corresponding magnetogram, have the best results compared to other lines. This methodology can be applicable to many scientific fields that use several different filter images.