• Proceedings of the Korean Information Science Society Conference
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• 2002.10c
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• pp.487-489
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• 2002

본 논문에서는 액티브코드를 이용한 실시간 역추적시스템에 대하여 논한다. 본 시스템은 우회공격의 연결특성을 이용하여 TCP 응용프로그램의 응답메시지에 액티브코드를 덧붙였다. 덧붙여진 액티브코드는 침입자의 근원지 소스측으로 실시간 이동하면서 네트워크 중간노드에서 침입자의 공격에 유연하게 대응한다. 또한, 본 시스템에서는 데이터은닉기법을 적용하여 중간 경유호스트에서 별도의 역추적 시스템을 도입할 필요가 없도록 기존의 환경에 투명성을 부여하였다. 이러한 방법을 통해, 기존의 호스트기반역추적 시스템의 신뢰성문제와 deployment문제를 해결하였다. 본 시스템을 통하여 기존의 네트워크환경에 최소한의 변경으로 침입자의 공격에 실시간적이며 능동적인 대응을 할 수 있다.

• Journal of Korean Tunnelling and Underground Space Association
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• v.14 no.4
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• pp.357-374
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• 2012

Considerable uncertainties are included in ground properties used for tunnel designs due to the limited investigation and tests. In this study, a back analysis was performed to find optimal ground properties based on artificial neural network using both face mapping data and convergence measurement data. First of all, the rock class of a study tunnel is determined from face mapping data. Then the possible ranges of ground properties were selected for each rock class through a literature review on the previous studies and utilized to establish more precise learning data. To find an optimal training model, a sensitivity analysis was also conducted by varying the number of hidden layers and the number of nodes more minutely than the previous study. As a result of this study, more accurate ground properties could be obtained. Therefore it was confirmed that the accuracy of the results could be increased by making use of not only convergence measurement data but also face mapping data in tunnel back analyses using artificial neural network. In future, it is expected that the methodology suggested in this study can be used to estimate ground properties more precisely.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.42 no.10
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• pp.884-892
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• 2014

In this paper, a meta model using neural network substituting for the simulator aerodynamic database is proposed to improve simulation fidelity near the critical flight area and real-time performance. It is shown that the accuracy of the meta model is relatively higher than the existing table lookup methods for arbitrary nonlinear database and the calculation speed is also improved for a specific F-16 maneuver simulation. The increase in the number of hidden nodes in the meta model for better accuracy of database representations causes a delay in function generation due to increased time required for computing exponential functions. In order to make up this drawback, we additionally study the fast exponential function method.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.16 no.3
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• pp.229-236
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• 2003

Artificial neural network has been used for damage assessment by many researchers, but there are still some barriers that must be overcome to improve its accuracy and efficiency. The major problems associated with the conventional artificial neural network, especially the Back Propagation Neural Network(BPNN), are on the need of many training patterns and on the ambiguous relationship between neural network architecture and the convergence of solution. Therefore, the number of hidden layers and nodes in one hidden layer would be determined by trial and error. Also, it takes a lot of time to prepare many training patterns and to determine the optimum architecture of neural network. To overcome these drawbacks, the PNN can be used as a pattern classifier. In this paper, the PNN is used numerically to detect damage in a plate girder railway bridge. Also, the comparison between mode shapes and natural frequencies of the structure is investigated to select the appropriate training pattern for the damage detection in the railway bridge.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.3
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• pp.32-43
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• 2002

In this paper, we propose a pattern classification system for digital signal which is based on neural networks. The proposed system consists of two models of neural network. The first part is a wavelet neural network whose role is a feature extraction. For this part, we compare existing models of wavelet networks and propose a new model for feature extraction. The other part is a wavelet network for pattern classification. We modify the structure of previous wavelet network for pattern classification and propose a learning method. The inputs of the pattern classification wavelet network is connection weights, dilation and translation parameters in hidden nodes of feature extraction network. And the output is a class of the signal which is input of feature extraction network. The proposed system is, applied to classification of EEG signal based on frequency.

• Journal of the Korean Society of Propulsion Engineers
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• v.25 no.6
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• pp.1-11
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• 2021

To develop an efficient procedure related to the flamelet library, the machine learning process based on artificial neural network(ANN) is applied for the gaseous hydrogen/liquid oxygen combustor under a supercritical pressure condition. For hidden layers, 25 combinations based on Rectified Linear Unit(ReLU) and hyperbolic tangent are adopted to find an optimum architecture in terms of the computational efficiency and the training performance. For activation functions, the hyperbolic tangent is proper to get the high learning performance for accurate properties. A transformation learning data is proposed to improve the training performance. When the optimal node is arranged for the 4 hidden layers, it is found to be the most efficient in terms of training performance and computational cost. Compared to the interpolation procedure, the ANN procedure reduces computational time and system memory by 37% and 99.98%, respectively.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.338-338
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• 2023

최근 특정 지역에 짧은 시간동안 많은 강우가 내리는 국지성 집중호우가 빈번히 발생하고 있으나, 이에 대한 예측과 대비에도 불구하고 피해는 지속적으로 증가하고 있다. 지속적인 강우량 증가 추이로 시간최대 및 일최대 강우량 관측기록이 해마다 갱신되고, 도시, 하천 및 주요 홍수방어 시설의 설계용량을 초과하는 피해가 발생하고 있다. 다수의 인구가 거주하고 대규모 기반시설이 집중된 도시지역에서 발생하는 집중호우는 심각한 인명 및 재산피해로 이어질 수 있다. 따라서, 부처별 재난의 저감대책은 정량적인 피해규모의 피해금액 예측보다는 설계 빈도에 대한 규모의 크기로 대책을 마련하고 있다. 국내에서는 풍수해 피해를 저감시키기 위해 개발에 따르는 재해영향요인을 개발 사업 시행 이전에 예측·분석하고 적절한 저감대책안을 수립·시행하고 있으나 설계빈도에 대한 규모일 뿐 정량적인 저감대책으로 예방되는 피해금액은 알 수 없다. 본 연구에서는 재해연보를 기반으로 호우재해(호우, 태풍)에 대한 시군구-재해기간의 피해데이터를 1999년부터 2019년까지 총 20년의 빅데이터와 전국 68개 강우관측소를 대상으로 총 20년(1999년 ~ 2019년)의 강우자료를 구축하였다. 머신러닝의 학습별 알고리즘을 조사하여 호우재해 피해데이터의 적용성이 높고 다양한 분야에 적용이 가능한 Neural networks의 분석기술인 ANN기법을 선정하였다 피해데이터의 재해발생기간별 총강우량, 일최대강우량, 총피해금액에 대하여 1999년 ~ 2018년을 학습하고 2019년에 대하여 강우특성과 피해특성의 분석하였다. 분석결과 Neural Networks의 지도학습은 총 6,902개 중 2019년을 제외한 6,414개를 학습하였으며 분석 타깃은 호우재해의 피해규모를 분석할 수 있는 총강우량, 일최대강우량, 총피해금액에 대하여 은닉노드 5개씩 2계층에 대하여 분석하였다.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.1
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• pp.95-107
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• 2017

In this study, a preliminary study was undertaken for development of a tunnel incident automatic detection system based on a machine learning algorithm which is to detect a number of incidents taking place in tunnel in real time and also to be able to identify the type of incident. Two road sites where CCTVs are operating have been selected and a part of CCTV images are treated to produce sets of training data. The data sets are composed of position and time information of moving objects on CCTV screen which are extracted by initially detecting and tracking of incoming objects into CCTV screen by using a conventional image processing technique available in this study. And the data sets are matched with 6 categories of events such as lane change, stoping, etc which are also involved in the training data sets. The training data are learnt by a resilience neural network where two hidden layers are applied and 9 architectural models are set up for parametric studies, from which the architectural model, 300(first hidden layer)-150(second hidden layer) is found to be optimum in highest accuracy with respect to training data as well as testing data not used for training. From this study, it was shown that the highly variable and complex traffic and incident features could be well identified without any definition of feature regulation by using a concept of machine learning. In addition, detection capability and accuracy of the machine learning based system will be automatically enhanced as much as big data of CCTV images in tunnel becomes rich.

• Journal of Korea Water Resources Association
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• v.33 no.2
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• pp.247-262
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• 2000

In this paper, the WSANN(Water Stage Analysis with Neural Network) model was presented so as to predict flood water stage at Jindong which has been the major stream gauging station in Nakdong river basin. The WSANN model used the improved backpropagation training algorithm which was complemented by the momentum method, improvement of initial condition and adaptive-learning rate and the data which were used for this study were classified into training and testing data sets. An empirical equation was derived to determine optimal hidden layer node between the hidden layer node and threshold iteration number. And, the calibration of the WSANN model was performed by the four training data sets. As a result of calibration, the WSANN22 and WSANN32 model were selected for the optimal models which would be used for model verification. The model verification was carried out so as to evaluate model fitness with the two-untrained testing data sets. And, flood water stages were reasonably predicted through the results of statistical analysis. As results of this study, further research activities are needed for the construction of a real-time warning of the impending flood and for the control of flood water stage with neural network method in river basin. basin.

• The Korean Journal of Applied Statistics
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• v.33 no.5
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• pp.615-625
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• 2020

The document-term frequency matrix is a term extracted from documents in which the group information exists in text mining. In this study, we generated the document-term frequency matrix for document classification according to research field. We applied the traditional term weighting function term frequency-inverse document frequency (TF-IDF) to the generated document-term frequency matrix. In addition, we applied term frequency-inverse gravity moment (TF-IGM). We also generated a document-keyword weighted matrix by extracting keywords to improve the document classification accuracy. Based on the keywords matrix extracted, we classify documents using a deep neural network. In order to find the optimal model in the deep neural network, the accuracy of document classification was verified by changing the number of hidden layers and hidden nodes. Consequently, the model with eight hidden layers showed the highest accuracy and all TF-IGM document classification accuracy (according to parameter changes) were higher than TF-IDF. In addition, the deep neural network was confirmed to have better accuracy than the support vector machine. Therefore, we propose a method to apply TF-IGM and a deep neural network in the document classification.