• KIPS Transactions on Software and Data Engineering
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• v.9 no.12
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• pp.403-410
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• 2020

In addition to the research on noise removal and super-resolution using the data restoration (Output result) function of Autoencoder, research on the performance improvement of clustering using the dimension reduction function of autoencoder are actively being conducted. The clustering function and data restoration function using Autoencoder have common points that both improve performance through the same learning. Based on these characteristics, this study conducted an experiment to see if the autoencoder model designed to have excellent data recovery performance is superior in clustering performance. Skip connection technique was used to design autoencoder with excellent data recovery performance. The output result performance and clustering performance of both autoencoder model with Skip connection and model without Skip connection were shown as graph and visual extract. The output result performance was increased, but the clustering performance was decreased. This result indicates that the neural network models such as autoencoders are not sure that each layer has learned the characteristics of the data well if the output result is good. Lastly, the performance degradation of clustering was compensated by using both latent code and skip connection. This study is a prior study to solve the Hanja Unicode problem by clustering.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.79-86
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• 2021

In order to increase the probability of success in big data projects, quantified techniques are required to analyze the root cause of risks from complex causes and establish optimal countermeasures. To this end, this study measures risk factors and relationships through SNA analysis and presents a way to respond to risks based on them. In other words, it derives a dependency network matrix by utilizing the results of correlation analysis between risk groups in the big data projects presented in the preliminary study and performs SNA analysis. In order to derive the dependency network matrix, partial correlation is obtained from the correlation between the risk nodes, and activity dependencies are derived by node by calculating the correlation influence and correlation dependency, thereby producing the causal relationship between the risk nodes and the degree of influence between all nodes in correlation. Recognizing the root cause of risks from networks between risk factors derived through SNA between risk factors enables more optimized and efficient risk management. This study is the first to apply SNA analysis techniques in relation to risk management response, and the results of this study are significant in that it not only optimizes the sequence of risk management for major risks in relation to risk management in IT projects but also presents a new risk analysis technique for risk control.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.253-265
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• 2020

As the risk of landslide is recently increasing due to the typhoons and localized heavy rains, effective techniques for the landslide damage detection are required to support the establishment of the recovery planning. This study describes the analysis of landslide-damaged areas using ISODATA(Iterative Self-Organizing Data Analysis Technique Algorithm) with Sentinel-2 image, regarding the case of Gokseong in August 7, 2020. A total of 4.75 ha of landslide-damaged areas was detected from the Sentinel-2 image using spectral characteristics of red, NIR(Near Infrared), and SWIR(Shortwave Infrared) bands. We made sure that the satellite remote sensing is an effective method to detect the landslide-damaged areas and support the establishment of the recovery planning, followed by the field surveys that require a lot of manpower and time. Also, this study can be used as a reference for the landslide management for the CAS500-1/2(Compact Advanced Satellite) scheduled to launch in 2021 and the Korean Medium Satellite for Agriculture and Forestry scheduled to launch in 2024.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.2
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• pp.40-51
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• 2021

The purpose of this study is to diagnose urban spatial structures using spatial modeling techniques for balanced urban development as part of sustainable urban growth management. Since urban spatial structure is an interaction of various activities, it is necessary to interpret the analysis results in conjunction with the analysis of changes in spatial structural elements. In this study, population and transportation were approached for research purposes. Population data were applied to the Getis-Ord G_i^* method, a spatial statistical technique, to analyze the concentration-decreasing region of the population. Traffic data analyzed the trend of centrality change by applying commuting traffic O-D data to Social Network Analysis techniques. The analysis showed that urban imbalance was growing, and the centrality of transportation was changing. The results of the analysis of spatial structure elements could be interpreted by linking the results of each factor to each neighborhood unit, predicting changes in urban spatial structure and suggesting directions for sustainable urban growth management.These results could also be used as a decision-making tool for various urban growth management policies introduced to cope with rapid urban development and uncontrollable development in many cities around the world.

• Journal of Environmental Analysis, Health and Toxicology
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• v.21 no.4
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• pp.264-280
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• 2018

In this study, volatile organic compounds (VOCs) were measured using a proton transfer reaction-time of flight-mass spectrometer (PTR-ToF-MS) at the Seoul Metropolitan Area Intensive Monitoring Station (SIMS) in Korea during the summer season of 2018. The results revealed that oxygenated VOCs (OVOCs) contributed a large fraction (83.6%) of the total VOCs, with methanol being the most abundant constituent (38.6%). The VOCs measured at SIMS were strongly influenced by local conditions. Non-volatile organic compounds (NVOCs), such as pinene, increased due to northeasterly wind direction in the morning, and OVOCs and anthropogenic VOCS (AVOCs) increased with northwesterly wind direction during the daytime. This was the result of the eastward location of Bukhansan National Park and the westward location of urban area from the SIMS location. The VOCs included abundant oxidized forms of VOCs, which can affect the generation of fine dust through various response pathways in the atmosphere. The real-time measurement technique using PTR-ToF-MS suggested in this study is expected to contribute to an improved scientific understanding of high-concentration fine dust events because the high temporal resolution makes it possible to analyze the variations of VOCs reflected in dynamic events.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.1
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• pp.63-70
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• 2009

Recently, in the security token based authentication system, there is an increasing trend of using fingerprint for the token holder verification, instead of passwords. However, the security of the fingerprint data is particularly important as the possible compromise of the data will be permanent. In this paper, we propose an approach for secure fingerprint verification by distributing both the secret and the computation based on the fuzzy vault(a cryptographic construct which has been proposed for crypto-biometric systems). That is, a user fingerprint template which is applied to the fuzzy vault is divided into two parts, and each part is stored into a security token and a server, respectively. At distributing the fingerprint template, we consider both the security level and the verification accuracy. Then, the geometric hashing technique is applied to solve the fingerprint alignment problem, and this computation is also distributed over the combination of the security token and the server in the form of the challenge-response. Finally, the polynomial can be reconstructed from the accumulated real points from both the security token and the server. Based on the experimental results, we confirm that our proposed approach can perform the fuzzy vault-based fingerprint verification more securely on a combination of a security token and a server without significant degradation of the verification accuracy.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1089-1098
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• 2020

In this study, we propose a method to detect red tide Cochlodinium Polykrikoide using by machine learning and geostationary marine satellite images. To learn the machine learning model, GOCI Level 2 data were used, and the red tide location data of the National Fisheries Research and Development Institute was used. The machine learning model used logistic regression model, decision tree model, and random forest model. As a result of the performance evaluation, compared to the traditional GOCI image-based red tide detection algorithm without machine learning (Son et al., 2012) (75%), it was confirmed that the accuracy was improved by about 13~22%p (88~98%). In addition, as a result of comparing and analyzing the detection performance between machine learning models, the random forest model (98%) showed the highest detection accuracy.It is believed that this machine learning-based red tide detection algorithm can be used to detect red tide early in the future and track and monitor its movement and spread.

• Journal of the Korea Society for Simulation
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• v.30 no.2
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• pp.11-22
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• 2021

Sonobuoy is widely used as a very important sensor in combat management system using P-3 patrol aircraft due to its advantages of rapid searching into wide exploration range. It is necessary to verify the performance of developed sonobuoy system using various maritime test data in order to be successfully applied in combat management system. But it is difficult to acquire various real maritime data because it needs much time and effort. Therefore we have developed in this paper a synthetic signal generator and a simulator that they can verify the performance of sonobuoy system and evaluate its operational effectiveness without conducting maritime test. We have synthesized target signals based on the characteristics of underwater sound sources, and then developed the synthesized signal generator which consider to sound propagation etc. like as underwater environment. And in the simulator development we use a HMI technique to enhance the convenience of operator, and design to verify the performance of sonobuoy system. The developed signal generator and simulator can be used as useful tools to evaluate the operational effectiveness such as optimal deployment of sonobuoy in combat management system using P-3 patrol aircraft.

• Journal of Plant Biotechnology
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• v.48 no.1
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• pp.34-43
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• 2021

Targeted genome editing using the CRISPR/Cas9 system is a ground-breaking technology that is being widely used to produce plants with useful traits. However, for woody plants, only a few successful attempts have been reported. These successes have used Agrobacterium-mediated transformation, which has been reported to be very efficient at producing genetically modified trees. Nonetheless, there are unresolved problems with plasmid sequences that remain in the plant genome. In this study, we demonstrated a DNA-free genome editing technique in which purified CRISPR/Cas9 ribonucleoproteins (RNPs) are delivered directly to the protoplasts of a hybrid poplar (Populus alba × Populus glandulosa). We designed three single-guide RNAs (sgRNAs) to target the stress-associated protein 1 gene (PagSAP1) in the hybrid poplar. Deep sequencing results showed that pre-assembled RNPs had a more efficient target mutagenesis insertion and deletion (indel) frequency than did non-assembled RNPs. Moreover, the RNP of sgRNA3 had a significantly higher editing efficacy than those of sgRNA1 and sgRNA2. Our results suggest that the CRISPR/Cas9 ribonucleoprotein-mediated transfection approach is useful for the production of transgene-free genome-edited tree plants.

• 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.