• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.9
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• pp.854-864
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• 2016

The use of small drone platform has become a popular topic in these days but its application for SAR operation has been little known due to the burden of the payload implementation. Drone platforms are distinguished from the conventional UAV system by the increased vulnerability to the turbulences, control-errors and poor motion stability. Consequently, sophisticated motion compensation may be required to guarantee the successful acquisition of high quality SAR imagery. Extremely limited power and mass budgets may prevent the use of additional hardwares for motion compensation and the difficulty of SAR focusing is further aggravated. In this paper, we have carried out a feasibility study of mico-SAR drone operation. We present the image acquisition results from the preliminary flight tests and a quality assessment is followed on the experimental SAR images. The in-flight motion errors derived from the unique drone movements are investigated and attempts have been made to compensate for the geometrical and phase errors caused by motions against the nominal trajectory. Finally, the successful operation of drone SAR system is validated through the focussed SAR images taken over test sites.

• Tunnel and Underground Space
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• v.30 no.3
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• pp.214-225
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• 2020

Uniaxial compressive strength (UCS) of rock is one of the important factors to determine the advance speed during shield TBM tunnel excavation. UCS can be obtained through the Geotechnical Data Report (GDR), and it is difficult to measure UCS for all tunneling alignment. Therefore, the purpose of this study is to predict UCS by utilizing TBM machine driving data and machine learning technique. Several machine learning techniques were compared to predict UCS, and it was confirmed the stacking model has the most successful prediction performance. TBM machine data and UCS used in the analysis were obtained from the excavation of rock strata with slurry shield TBMs. The data were divided into 8:2 for training and test and pre-processed including feature selection, scaling, and outlier removal. After completing the hyper-parameter tuning, the stacking model was evaluated with the root-mean-square error (RMSE) and the determination coefficient (R²), and it was found to be 5.556 and 0.943, respectively. Based on the results, the sacking models are considered useful in predicting rock strength with TBM excavation data.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.5
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• pp.525-534
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• 2016

In South Korea, paddy rice has been consumed over the entire region and it is the main source of income for farmers, thus mathematical model for the estimation of rice yield is required for such decision-making processes in agriculture. The objectives of our study are to: (1) develop rice yield estimation model using Convolutional Neural Networks(CNN), (2) choose hyper-parameters for the model which show the best performance and (3) investigate whether CNN model can effectively predict the rice yield by the comparison with the model using Artificial Neural Networks(ANN). Weather and MODIS(The MOderate Resolution Imaging Spectroradiometer) products from April to September in year 2000~2013 were used for the rice yield estimation models and cross-validation was implemented for the accuracy assessment. The CNN and ANN models showed Root Mean Square Error(RMSE) of 36.10kg/10a, 48.61kg/10a based on rice points, respectively and 31.30kg/10a, 39.31kg/10a based on 'Si-Gun-Gu' districts, respectively. The CNN models outperformed ANN models and its possibility of application for the field of rice yield estimation in South Korea was proved.

• Journal of the Korea Computer Industry Society
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• v.3 no.3
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• pp.331-340
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• 2002

The web world is getting so huge and untractable that without an intelligent information extractor we would get more and more helpless. Conventional web spidering techniques for general purpose search engine may be too slow for the specific search engines, which concentrate only on specific areas or keywords. In this paper a new model for improving web spidering capabilities is suggested and experimented. How to select adequate reference web pages from the initial web Page set relevant to a given specific area (or keywords) can be very important to reduce the spidering speed. Our reference web page selection method DOPS dynamically and orthogonally selects web pages, and it can also decide the appropriate number of reference pages, using a newly defined measure. Even for a very specific area, this method worked comparably well almost at the level of experts. If we consider that experts cannot work on a huge initial page set, and they still have difficulty in deciding the optimal number of the reference web pages, this method seems to be very promising. We also applied reinforcement learning to web environment, and DOPS-based reinforcement learning experiments shows that our method works quite favorably in terms of both the number of hyper links and time.

• The Journal of the Korea Contents Association
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• v.19 no.10
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• pp.315-324
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• 2019

In this research, one of five Hofstede's cultural dimension, Long term orientation (LTO) was selected to examine its influence on individual's change oriented organizational citizenship behavior with emotional regulation mediating. East Asian countries and Korea especially received higher score on LTO cultural dimension, meaning it valued harmony, long term relations and saving for future. Also, in today's hyper competitive and evolving global climate, the ability to adapt quickly and also to be able to control one's emotion is highly valued individual competency. Previous research on Hofstede's cultural dimensions were conducted at mostly national or large group levels. However, in this study, Yoo's CVSCALE which allowed for individual level analysis on Hofstede's cultural dimensions were used to analyze multinational company's employees's long term orientations' influence on emotional regulation and change oriented organizational citizenship behavior. The survey conducted from 200 employees from major electronic company based in S city in Korea for two weeks period and the results indicated long term orientation positively influenced change oriented organizational citizenship behavior. Also emotional regulation mediated between long term orientation felt by individuals and change oriented organizational citizenship behavior. Such results validated previous studies that indicated emotional regulation as possible antecedents of individual proactive behaviors such as change organizational citizenship behavior and long term oriented view as another potential antecedent of change oriented organizational citizenship behavior in multinational corporation setting.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.5
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• pp.574-583
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• 2021

Predicting shipping markets is an important issue. Such predictions form the basis for decisions on investment methods, fleet formation methods, freight rates, etc., which greatly affect the profits and survival of a company. To this end, in this study, we propose a shipping freight rate prediction model for container ships using gated recurrent units (GRUs) and long short-term memory structure. The target of our freight rate prediction is the China Container Freight Index (CCFI), and CCFI data from March 2003 to May 2020 were used for training. The CCFI after June 2020 was first predicted according to each model and then compared and analyzed with the actual CCFI. For the experimental model, a total of six models were designed according to the hyperparameter settings. Additionally, the ARIMA model was included in the experiment for performance comparison with the traditional analysis method. The optimal model was selected based on two evaluation methods. The first evaluation method selects the model with the smallest average value of the root mean square error (RMSE) obtained by repeating each model 10 times. The second method selects the model with the lowest RMSE in all experiments. The experimental results revealed not only the improved accuracy of the deep learning model compared to the traditional time series prediction model, ARIMA, but also the contribution in enhancing the risk management ability of freight fluctuations through deep learning models. On the contrary, in the event of sudden changes in freight owing to the effects of external factors such as the Covid-19 pandemic, the accuracy of the forecasting model reduced. The GRU1 model recorded the lowest RMSE (69.55, 49.35) in both evaluation methods, and it was selected as the optimal model.

• Journal of IKEEE
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• v.23 no.2
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• pp.438-447
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• 2019

As the IoT-based hyper-connected society grows, public-key cryptosystem such as RSA is frequently used for encryption, authentication, and digital signature. Public-key cryptosystem use very large (safe) prime numbers to ensure security against malicious attacks. Even though the performance of the device has greatly improved, the generation of a large (safe)prime is time-consuming or memory-intensive. In this paper, we propose ET-MR and ET-MR-MR using Euler sieve so it runs faster while using less memory. We present a running time prediction model by probabilistic analysis and compare time and memory of our method with conventional methods. Experimental results show that the difference between the expected running time and the measured running time is less than 4%. In addition, the fastest running time of ET-MR is 36% faster than that of TD-MR, 8.5% faster than that of DT-MR and the fastest running time of ET-MR-MR is 65.3% faster than that of TD-MR-MR and similar to that of DT-MR-MR. When k=12,381, the memory usage of ET-MR is 2.7 times more than that of DT-MR but 98.5% less than that of TD-MR and when k=65,536, the memory usage of ET-MR-MR is 98.48% less than that of TD-MR-MR and 92.8% less than that of DT-MR-MR.

• Journal of Navigation and Port Research
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• v.43 no.1
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• pp.72-78
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• 2019

The second-hand ship market provides immediate access to the freight market for shipping investors. When introducing second-hand vessels, the precise estimate of the price is crucial to the decision-making process because it directly affects the burden of capital cost to investors in the future. Previous studies on the second-hand market have mainly focused on the market efficiency. The number of papers on the estimation of second-hand vessel values is very limited. This study proposes an artificial neural network model that has not been attempted in previous studies. Six factors, freight, new-building price, orderbook, scrap price, age and vessel size, that affect the second-hand ship price were identified through literature review. The employed data is 366 real trading records of Panamax second-hand vessels reported to Clarkson between January 2016 and December 2018. Statistical filtering was carried out through correlation analysis and stepwise regression analysis, and three parameters, which are freight, age and size, were selected. Ten-fold cross validation was used to estimate the hyper-parameters of the artificial neural network model. The result of this study confirmed that the performance of the artificial neural network model is better than that of simple stepwise regression analysis. The application of the statistical verification process and artificial neural network model differentiates this paper from others. In addition, it is expected that a scientific model that satisfies both statistical rationality and accuracy of the results will make a contribution to real-life practices.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.5
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• pp.36-46
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• 2019

The number of hosts connected to the Internet has increased dramatically, introducing the Domain Name System(DNS) in 1984. DNS is now an important key point for all users of the Internet by allowing them to use a convenient character address without memorizing a series of numbers of complex IP address. However, relative to the importance of DNS, there still exist many problems such as the authorization allocation issue, the disputes over public registration, security vulnerability such as DNS cache poisoning, DNS spoofing, man-in-the-middle attack, DNS amplification attack, and the need for many domain names in the age of hyper-connected networks. In this paper, to effectively improve these problems of existing DNS, we proposed a method of implementing DNS using distributed ledger technology, blockchain, and implemented using a Ethereum-based platform. In addition, the qualitative analysis performance comparative evaluation of the existing domain name registration and domain name server was conducted, and conducted security assessments on the proposed system to improve security problem of existing DNS. In conclusion, it was shown that DNS services could be provided high security and high efficiently using blockchain.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.105-111
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• 2019

Core technologies that lead the Fourth Industrial Revolution era, such as artificial intelligence, big data, and autonomous driving, are implemented and serviced through the rapid development of computing power and hyper-connected networks based on the Internet of Things. In this paper, we implement two different models for drivable area segmentation in various environment, and propose a better model by comparing the results. The models for drivable area segmentation are using DeepLab V3+ and Mask R-CNN, which have great performances in the field of image segmentation and are used in many studies in autonomous driving technology. For driving information in various environment, we use BDD dataset which provides driving videos and images in various weather conditions and day&night time. The result of two different models shows that Mask R-CNN has higher performance with 68.33% IoU than DeepLab V3+ with 48.97% IoU. In addition, the result of visual inspection of drivable area segmentation on driving image, the accuracy of Mask R-CNN is 83% and DeepLab V3+ is 69%. It indicates Mask R-CNN is more efficient than DeepLab V3+ in drivable area segmentation.