• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.6 no.2
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• pp.155-162
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• 2008

Inventories to be disposed of, reference turnup, and source terms for CANDU spent fuel were evaluated for geological disposal system design. The historical and projected inventory by 2040 is expected to be 14,600 MtU under the condition of 30-year lifetime for unit 1 and 40-year lifetime for other units in Wolsong site. As a result of statistical analysis for discharge burnup of the spent fuels generated by 2007, average and stand deviation revealed 6,987 MWD/MtU and 1,167, respectively. From this result, the reference burnup was determined as 8,100 MWD/MtU which covers 84% of spent fuels in total. Source terms such as nuclide concentration for a long-term safety analysis, decay heat, thermo-mechanical analysis, and radiation intenity and spectrum was characterized by using ORIGEN-ARP containing conservativeness in the aspect of decay heat up to several thousand years. The results from this study will be useful for the design of storage and disposal facilities.

• Journal of Korean Society of Transportation
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• v.27 no.5
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• pp.97-111
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• 2009

Image-based traffic information collection systems have entered widespread adoption and use in many countries since these systems are not only capable of replacing existing loop-based detectors which have limitations in management and administration, but are also capable of providing and managing a wide variety of traffic related information. In addition, these systems are expanding rapidly in terms of purpose and scope of use. Currently, the utilization of image processing technology in the field of traffic accident management is limited to installing surveillance cameras on locations where traffic accidents are expected to occur and digitalizing of recorded data. Accurately recording the sequence of situations around a traffic accident in a signal intersection and then objectively and clearly analyzing how such accident occurred is more urgent and important than anything else in resolving a traffic accident. Therefore, in this research, we intend to present a technology capable of overcoming problems in which advanced existing technologies exhibited limitations in handling real-time due to large data capacity such as object separation of vehicles and tracking, which pose difficulties due to environmental diversities and changes at a signal intersection with complex traffic situations, as pointed out by many past researches while presenting and implementing an active and environmentally adaptive methodology capable of effectively reducing false detection situations which frequently occur even with the Gaussian complex model analytical method which has been considered the best among well-known environmental obstacle reduction methods. To prove that the technology developed by this research has performance advantage over existing automatic traffic accident recording systems, a test was performed by entering image data from an actually operating crossroad online in real-time. The test results were compared with the performance of other existing technologies.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.128-128
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• 2011

본 연구에서는 자동유량측정시설에 의하여 실시간으로 생산되는 자동유량측정 자료의 정상성 여부를 판단하는데 중요한 적정 측정 신뢰구간을 실시간으로 예측할 수 있는 기술을 개발하였다. 전세계적으로, 현대적인 유량측정이 시작된 이래 연속유량 산정을 위한 방법은 수위-유량관계곡선을 이용하는 방법 외에 실무적으로 활용 가능한 방법은 거의 전무한 실정이다. 수위-유량관계곡선을 이용하는 방법은 연속수위를 계측하여 이에 해당하는 연속유량을 산정하는 방법으로 수위와 유량간에 일정한 관계를 가지는 정상적인 흐름을 보이는 자연하천의 경우에 정확도가 매우 높다. 그러나 감조나 구조물 등에 의해 유량이 조절되는 경우에 유량산정의 정확도는 현저히 떨어지게 된다. 따라서 수위에서 유량을 환산하는 방법이 아닌 유량을 직접 연속으로 측정하는 방법이 꾸준히 연구되어 왔고, 이 중 가장 대표적인 방법이 자동유량측정 방법이다. 그러나 자동유량측정 방법은 유량을 연속으로 측정할 수 있다는 장점에 반해 측정된 유량의 정확도를 높이기가 매우 어렵다는 단점도 가지고 있다. 계측 자체의 기술적 한계는 주로 계측기기적인 문제로 이는 전자기, 통신 기술 등 첨단 기술의 발전과 함께 다양한 현장 시험을 통해 폭넓은 개선이 이루어지고 있다. 그러나 아직 기술적 완성도가 완전하지 못한 현실에서, 현재 설치되어 있는 자동유량측정 유량자료의 신뢰도를 높이기 위해서는 각각의 계측 시점에서 자료가 정상적으로 산정되고 있는지에 대한 검정이 필요하고, 이는 자동유량측정 자료의 정확도 확보에 매우 중요한 관건으로 작용할 수밖에 없다. 이러한 배경에서 본 연구에서는 조석성분과 유출성분을 분리하여 예측하는 방법을 새롭게 개발 적용하였다. 자료는 자료의 시간해상도 증감에 따른 실제 예측의 정확도 증감을 고려하여 가장 적절하다고 판단되는 시자료를 사용하였으며, 자료간 상관을 분석하여 주 입력 자료로 팔당댐 방류량, 한강대교 지점 수위, 전류 수위를 이용하였다. 모형의 예측 능력을 극대화하기 위하여 조석 영향을 받는 자료의 경우는 웨이블릿 변환(wavelet transform)을 이용하여 순수 유출성분과 조위성분을 분리하여 별도로 적용하였다. 그리고 예측을 위한 모형은 실시간 자료기반 모형으로 그 안정성이 인정된 서포트벡터머신(support vector machine)을 이용하였다. 이러한 과정을 통해 한강대교 지점의 순수 유출성분과 조위성분의 유량을 각각 예측한 후 두 결과를 합성하여 최종 한강 대교 지점의 유량을 산정하였다. 조석성분을 분리하여 한강대교 지점의 유량을 예측한 결과 대부분의 예측치가 95% 예측구간에 포함되었다. 그리고 조석성분을 분리하지 않은 모형과 조석성분을 분리한 모형의 예측 능력을 비교한 결과, 조석성분을 분리한 모형이 예측이 정확도가 높았다. RMSE의 경우 분리하지 않은 모형대비 23%의 예측오차가 감소하였고, NSC의 경우 0.92에서 0.95로 예측의 정확도가 증가하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.35-35
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• 2017

유역 물순환 개선 기술은 기후변화 및 토지이용변화가 진행 중에 있거나 예상되는 지역에 대하여 강우-유출수를 지연, 저류, 침투시켜 지속가능한 물순환체계를 유지?회복하도록 하는 기술이라 할 수 있다. 기후변화 및 토지이용변화에 따른 영향을 평가하고, 지속가능한 유역 물순환체계를 구축하여 적응 전략을 체계적으로 수립하기 위해서는 물순환 개선 기술의 정립 및 개발이 필수적이다. 그러나 현재 유역 물순환 개선기술은 일부 시가화 지역에 국한되어 있어 유역 규모의 개선 전략을 수립하는데 한계가 있다. 또한, 물순환계의 변화량을 평가하기 위한 모형화 기법 역시 해외에서 개발된 기술을 여과 없이 도입하여 국내의 환경을 충분히 반영하기는 어렵다. 개발된 유역 물순환 개선 및 평가시스템은 기존 국가연구개발사업을 통해 개발되고 사업화에 성공한 바 있는 유역 물순환 평가 모형인 CAT(Catchment hydrologic cycle Assessment Tool)을 수정 및 개선하여 기후변화에 따른 영향을 평가하고 적응 대책을 수립하기 위한 실무적인 소프트웨어이다. 침투트렌치, 식생침투트렌치, 습지, 저류지, 빗물탱크 등의 물순환개선시설에 대한 효과를 평가할 수 있도록 개별시설의 제원에 따른 물순환개선 효과를 정량적으로 평가하여 제시한다. 기후변화에 따른 장기간의 유역 물순환을 평가하기 위해서는 물리적 매개변수 기반의 수문해석 보다는 단순환된 개념적 매개변수 기반의 집중형 장기유출 해석이 필요할 수 있다. 따라서 국내외에서 많이 사용되고 있는 장기 일유출 모형(GR4J, GSM, HBV, SYMHYD, TANK, TPHM 등)을 유역 물순환 개선 평가 플랫폼에 탑재함으로써 소유역의 특성을 반영한 기후변화 적응 일유출 해석이 가능하도록 하였으며, 각 모형들의 매개변수는 수동보정 외에도 SCE-UA를 이용한 자동보정이 가능하도록 시스템으로 구축하였다. 개발된 유역물순환 개선 및 평가시스템은 실무적 차원에서 기후변화에 따른 유역 물순환 개선 기술을 적용하고 평가하는데 있어서 매개변수 입력자료 구축에 따른 자원 소요 시간 및 시스템 개발 비용을 획기적으로 단축시켰으며 국외 의존도가 높은 수문 해석모형을 국내 기술로 개발함으로써 기술자립도를 높이고 국내 및 해외의 유역의 성공적인 적용을 통하여 성능을 입증하였다. 기후변화에 따른 수자원의 재평가, 개선시설의 정량적 평가 및 하천유역의 수자원관리 실무에 적용성이 높을 것으로 기대된다.

• Tunnel and Underground Space
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• v.28 no.6
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• pp.596-608
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• 2018

As urban infrastructure is aging, the possibility of accidents due to the failures or breakdowns of infrastructure increases. Especially, aging underground infrastructures like sewer pipes, waterworks, and subway have a potential to cause an urban ground sink. Urban ground sink is defined just as a local and erratic collapse occurred by underground cavity due to soil erosion or soil loss, which is separated from a sinkhole in soluble bedrock such as limestone. The conventional measurements such as differential settlement gauge, inclinometer or earth pressure gauge have a shortcoming just to provide point measurements with short coverage. Therefore, these methods are not adequate for monitoring of an erratic subsidence caused by underground cavity due to soil erosion or soil loss which occurring at unspecified time and location. Therefore, an alternative technology is required to detect a change of underground physical condition in real time. In this study, the feasibility of a novel magnetic resonance based monitoring method is investigated through laboratory tests, where the changes of path loss (S21) were measured under various testing conditions: media including air, water, and soil, resonant frequency, impedance, and distances between transmitter (TX) and receiver (RX). Theoretically, the transfer characteristic of magnetic field is known to be independent of the density of the medium. However, the results of the test showed the meaningful differences in the path loss (S21) under the different conditions of medium. And it is found that the reflection coefficient showed the more distinct differences over the testing conditions than the path loss. In particular, input reflection coefficient (S11) is more distinguishable than output reflection coefficient (S22).

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.2
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• pp.93-101
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• 2019

The objective of this study is to develop a model which can predict the seismic performance of the slope relatively accurately and efficiently by using artificial neural network(ANN) technique. The quantification of such the seismic performance of the slope is not easy task due to the randomness and the uncertainty of the earthquake input and slope model. Under these circumstances, probabilistic seismic fragility analyses of slope have been carried out by several researchers, and a closed-form equation for slope seismic performance was proposed through a multiple linear regression analysis. However, a traditional statistical linear regression analysis has shown a limit that cannot accurately represent the nonlinearistic relationship between the slope of various conditions and seismic performance. In order to overcome these problems, in this study, we attempted to apply the ANN to generate prediction models of the seismic performance of the slope. The validity of the derived model was verified by comparing this with the conventional multi-linear and multi-nonlinear regression models. As a result, the models obtained through the ANN basically showed excellent performance in predicting the seismic performance of the slope, compared to the models obtained by the statistical regression analyses of the previous study.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.1-9
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• 2021

Traditionally, most malicious codes have been analyzed using feature information extracted by domain experts. However, this feature-based analysis method depends on the analyst's capabilities and has limitations in detecting variant malicious codes that have modified existing malicious codes. In this study, we propose a ResNet-Variational AutoEncder-based variant malware classification method that can classify a family of variant malware without domain expert intervention. The Variational AutoEncoder network has the characteristics of creating new data within a normal distribution and understanding the characteristics of the data well in the learning process of training data provided as input values. In this study, important features of malicious code could be extracted by extracting latent variables in the learning process of Variational AutoEncoder. In addition, transfer learning was performed to better learn the characteristics of the training data and increase the efficiency of learning. The learning parameters of the ResNet-152 model pre-trained with the ImageNet Dataset were transferred to the learning parameters of the Encoder Network. The ResNet-Variational AutoEncoder that performed transfer learning showed higher performance than the existing Variational AutoEncoder and provided learning efficiency. Meanwhile, an ensemble model, Stacking Classifier, was used as a method for classifying variant malicious codes. As a result of learning the Stacking Classifier based on the characteristic data of the variant malware extracted by the Encoder Network of the ResNet-VAE model, an accuracy of 98.66% and an F1-Score of 98.68 were obtained.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1115-1130
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• 2020

Modern vehicles are equipped with a number of ECUs(Electronic Control Units), and ECUs can control vehicles efficiently by communicating each other through CAN(Controller Area Network). However, CAN bus is known to be vulnerable to cyber attacks because of the lack of message authentication and message encryption, and access control. To find these security issues related to vehicle hacking, CAN Fuzzing methods, that analyze the vulnerabilities of ECUs, have been studied. In the existing CAN Fuzzing methods, fuzzing inputs are randomly generated without considering the structure of CAN messages transmitted by ECUs, which results in the non-negligible fuzzing time. In addition, the existing fuzzing solutions have limitations in how to monitor fuzzing results. To deal with the limitations of CAN Fuzzing, in this paper, we propose a Non-Random CAN Fuzzing, which consider the structure of CAN messages and systematically generates fuzzing input values that can cause malfunctions to ECUs. The proposed Non-Random CAN Fuzzing takes less time than the existing CAN Fuzzing solutions, so it can quickly find CAN messages related to malfunctions of ECUs that could be originated from SW implementation errors or CAN DBC(Database CAN) design errors. We evaluated the performance of Non-Random CAN Fuzzing by conducting an experiment in a real vehicle, and proved that the proposed method can find CAN messages related to malfunctions faster than the existing fuzzing solutions.

• Journal of Korean Society of Disaster and Security
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• v.13 no.4
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• pp.75-92
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• 2020

In most cases of the water balance analysis, the return flow ratio for each water supply was uniformly determined and applied, so it has been contained a problem that the volume of available water would be incorrectly calculated. Therefore, sewage and wastewater among the return water were focused in this study and the data-driven model was developed to forecast the outflow from the sewage treatment plant. The forecasting results of LSTM (Long Short-Term Memory), GRU (Gated Recurrent Units), and SVR (Support Vector Regression) models, which are mainly used for forecasting the time series data in most fields, were compared with the observed data to determine the optimal model parameters for forecasting outflow. As a result of applying the model, the root mean square error (RMSE) of the GRU model was smaller than those of the LSTM and SVR models, and the Nash-Sutcliffe coefficient (NSE) was higher than those of others. Thus, it was judged that the GRU model could be the optimal model for forecasting the outflow in sewage treatment plants. However, the forecasting outflow tends to be underestimated and overestimated in extreme sections. Therefore, the additional data for extreme events and reducing the minimum time unit of input data were necessary to enhance the accuracy of forecasting. If the water use of the target site was reviewed and the additional parameters that could reflect seasonal effects were considered, more accurate outflow could be forecasted to be ready for climate variability in near future. And it is expected to use as fundamental resources for establishing a reasonable river water management system based on the forecasting results.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.3
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• pp.141-148
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• 2022

Recently, reinforcement learning combined with deep neural network technology has achieved remarkable success in various fields such as board games such as Go and chess, computer games such as Atari and StartCraft, and robot object manipulation tasks. However, such deep reinforcement learning describes states, actions, and policies in vector representation. Therefore, the existing deep reinforcement learning has some limitations in generality and interpretability of the learned policy, and it is difficult to effectively incorporate domain knowledge into policy learning. On the other hand, dNL-RRL, a new relational reinforcement learning framework proposed to solve these problems, uses a kind of vector representation for sensor input data and lower-level motion control as in the existing deep reinforcement learning. However, for states, actions, and learned policies, It uses a relational representation with logic predicates and rules. In this paper, we present dNL-RRL-based policy learning for transportation mobile robots in a manufacturing environment. In particular, this study proposes a effective method to utilize the prior domain knowledge of human experts to improve the efficiency of relational reinforcement learning. Through various experiments, we demonstrate the performance improvement of the relational reinforcement learning by using domain knowledge as proposed in this paper.