• Proceedings of the Korean Nuclear Society Conference
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• 1999.10a
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• pp.272-272
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• 1999

• Journal of Korean Society of Forest Science
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• v.110 no.4
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• pp.610-621
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• 2021

The soil creep, primarily caused by earthquakes and torrential rainfall events, has widely occurred across the country. The Korea Forest Service attempted to quantify the soil creep susceptible areas using a discriminant value table to prevent or mitigate casualties and/or property damages in advance. With the advent of advanced computer technologies, machine learning-based classification models have been employed for managing mountainous disasters, such as landslides and debris flows. This study aims to quantify the soil creep susceptibility using several classifiers, namely the k-Nearest Neighbor (k-NN), Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM) models. To develop the classification models, we downscaled 292 data from 4,618 field survey data. About 70% of the selected data were used for training, with the remaining 30% used for model testing. The developed models have the classification accuracy of 0.727 for k-NN, 0.750 for NB, 0.807 for RF, and 0.750 for SVM against test datasets representing 30% of the total data. Furthermore, we estimated Cohen's Kappa index as 0.534, 0.580, 0.673, and 0.585, with AUC values of 0.872, 0.912, 0.943, and 0.834, respectively. The machine learning-based classifications for soil creep susceptibility were RF, NB, SVM, and k-NN in that order. Our findings indicate that the machine learning classifiers can provide valuable information in establishing and implementing natural disaster management plans in mountainous areas.

• Transactions of Materials Processing
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• v.26 no.1
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• pp.48-54
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• 2017

The dissolution and precipitation of Nb, which has been known as strong carbide-forming element, play a key role in controlling phase transformation kinetics of microalloyed steels. In this study, we analyzed both numerically and experimentally the precipitation behavior of Nb-microalloyed steel and its effect on the austenite decomposition during cooling. Nb precipitation in austenite matrix could be predicted by the thermo-kinetic software MatCalc, in which interfacial energy between precipitate and matrix is calculated. The simulated precipitation kinetics fairly well agrees with the experimental observations by TEM. Austenite decomposition, which is strongly affected by Nb precipitation during cooling, was measured by dilatometry and was modeled on the basis of a Johnson-Mehl-Avrami-Kolmorgorov(JMAK) equation. It was confirmed that the dissolved Nb delays the austenite decomposition, whereas, the precipitated Nb accelerates phase transformation during the austenite decomposition.

• Proceedings of the Korean Nuclear Society Conference
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• 1995.05a
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• pp.765-770
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• 1995

지체균열전파(DHC)는 중수로 압력관의 수명에 근 영향을 미치는 중요한 현상 중의 하나이다. 본 연구에서는 열처리를 통하여 압력관 재료인 Zr-2,5Nb의 기계적 성질, 집합조직을 변화시켜 각 인자들이 DHC에 미치는 영향을 조사하였다. 그 결과 지체균열전파속도(DHCV)는 항복강도와 경도와 비례한다는 것과 유사한 미세구조와 집합조직을 갖는 Zr-2.5Nb의 경우 항복강도와 Puls의 모델을 이용하여 지체균열전파속도(DHCV)를 예측할 수 있었다. 그리고 secondary cracking이 annealing한 시편들에서는 관찰되었으나 $\beta$열처리 후 급냉한 시편에서는 관찰되지 않았다. 이것의 수소화물 형상의 차이에 의한 것으로 생각된다.

• Journal of the Korean Society of Radiology
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• v.9 no.6
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• pp.417-420
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• 2015

We obtained Surviving Fraction (SF) after irradiation carbon beam to compare the applicability of the Linear-Quadratic model, Incomplete Repair model, Marchese model. Mathematica software(ver 9.0) used to calcurate parameters and compared result. LQ model could not explain the entire response of fractionated carbon beam irradiation. It becomes necessary to construct models that extend the LQ model of conventional radiotherapy for the carbon beam therapy. By combining both Potentially Lethal Damage Repair (PLDR) and Sublethal Damage Repair (SLDR) a new LQ model can develop that aptly modeled the cellular response to fractionated irradiation.

• Convergence Security Journal
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• v.24 no.3
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• pp.41-49
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• 2024

A tokenizer, which is a key component of the Transformer model, lacks the ability to effectively comprehend numerical data. Therefore, to develop a Transformer-based intrusion detection model that can operate within a real-world network environment by training packet payloads as sentences, it is necessary to convert the hexadecimal packet payloads into a character-based format. In this study, we applied three character encoding methods to convert packet payloads into numeric or character format and analyzed how detection performance changes when training them on transformer architecture. The experimental dataset was generated by extracting packet payloads from PCAP files included in the UNSW-NB15 dataset, and the RoBERTa was used as the training model. The experimental results demonstrate that the ISO-8859-1 encoding scheme achieves the highest performance in both binary and multi-class classification. In addition, when the number of tokens is set to 512 and the maximum number of epochs is set to 15, the multi-class classification accuracy is improved to 88.77%.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.6 no.3
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• pp.327-340
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• 1996

In this study, a series of $LiNbO_{3}$ crystals with different [Li]/[Nb] ratios, congruent $LiNbO_{3}$ crystals with doped Mg and with Mg and codoped with Mn were grown by the Czocharalski method. These were investigated by UV and IR spectrophotometry. Stoichiometry dependences of the UV absorption edge and the $OH^{-}$ absorption spectra were studied with different [Li]/[Nb] ratios. The position of the UV absorption edge adn the shape and peak point of the $OH^{-}$ absorption spectra changed monotonously upto a critical concentration of Mg ions. The mechanism of the incorporation of Mg ions changes at this concentration. The decomposition of the $OH^{-}$ absorption spectra using a Gaussian lineshape function showed that in Li-deficient crystals the absorption spectra consist of five components in contrast to more or less perfect stoichiometric crystals which reveal to three components. On the basis of these results, the intrinsic and the extrinsic defect structure models in $LiNbO_{3}$ crystals were examined. The behaviour of $\nu$ (OH) reflects the defect structure and supports the Li-site vacancy model as the intrinsic defect structure model and the corresponding extrinsic defect model. A brief discussion is also given of the behaviour of $\nu$ (OH) in $LiNbO_{3}$ crystals simultaneously doped with several kinds of impurity.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.81-87
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• 2023

Intrusion detection systems that learn metadata of network packets have been proposed recently. However these approaches require time to analyze packets to generate metadata for model learning, and time to pre-process metadata before learning. In addition, models that have learned specific metadata cannot detect intrusion by using original packets flowing into the network as they are. To address the problem, this paper propose a natural language processing-based intrusion detection system that detects intrusions by learning the packet payload as a single sentence without an additional conversion process. To verify the performance of our approach, we utilized the UNSW-NB15 and Transformer models. First, the PCAP files of the dataset were labeled, and then two Transformer (BERT, DistilBERT) models were trained directly in the form of sentences to analyze the detection performance. The experimental results showed that the binary classification accuracy was 99.03% and 99.05%, respectively, which is similar or superior to the detection performance of the techniques proposed in previous studies. Multi-class classification showed better performance with 86.63% and 86.36%, respectively.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.3
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• pp.617-623
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• 2018

This study focuses on detection of malicious code through AndroidManifest permissoion feature extracted based on Android static analysis. Features are built on the permissions of AndroidManifest, which can save resources and time for analysis. Malicious app detection model consisted of SVM (support vector machine), NB (Naive Bayes), Gradient Boosting Classifier (GBC) and Logistic Regression model which learned 1,500 normal apps and 500 malicious apps and 98% detection rate. In addition, malicious app family identification is implemented by multi-classifiers model using algorithm SVM, GPC (Gaussian Process Classifier) and GBC (Gradient Boosting Classifier). The learned family identification machine learning model identified 92% of malicious app families.

• Proceedings of the Korean Nuclear Society Conference
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• 1998.05b
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• pp.915-920
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• 1998

본 논문에서는 반구형 헤드(hemi-spherical head)를 가진 압력용기에 비방사형(non-radial) 노즐을 가공할 경우, 개구(opening) 간격이 반구형 헤드의 설계에 미치는 영향을 검토하기 위하여 개구 간격의 변화에 따른 응력분포변화를 분석하였다. ASME 코드는 NB-3222.4(d)의 설계 조건을 만족하는 압력 용기의 혜드에 노즐을 가공할 경우, NB-3338.2(d)에서 개구사이의 최소거리를 제시하고 있다. 본 논문에 서는 ASME 코드가 제시하고 있는 개구사이의 최소거리의 타당성과 설계상 이 요건을 만족하지 못하는 경우에 대하여 분석하고 검토하였다. 해석모델은 한국 표준형원자로의 가압기를 기본모델로하여 개구사이의 간격변화에 따른 응력변화를 검토하고, 설계시 고려하여야할 인자를 분석하였다.