• 한국지반공학회지:지반
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• 제12권5호
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• pp.17-26
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• 1996

본 연구에서는 아산만 지역의 해안 연약층에서 채취한 해성토의 거동을 자동화된 삼축실험기를 사용하여 비배수 상태하에서 실내실험과 모델예측을 수행하여 파괴전과 파괴상태시 응력-변형률 상태를 점토질 및 실트질 흙과 비교분석하였다. 채취한 해성토는 실트질 70%와 점토질 30%가 섞인 혼합토로 현장의 비교란 시료와 이를 재 성형한 시료의 2종류로 만들어 400kpa인 유효구속압력까지 등방압밀 시킨 후 압밀하중을 감소시키며 구속압이 각각 400, 200, 100, 67kpa인 경우에 비배수 상태로 삼축압축 및 인장실험을 하였다. 본 연구결과 모든 시료의 극한상태를 연결하면 일정한 파괴선에 도달하였으며 이때 파괴선은 순수점토나 실트에 비해서는 그 기울기에 차이가 있었다. 또한 정규압밀된 아산만 해성토에서는 전단초기에는 순수점토와 유사하게 전단하에서는 양의 간극수압이 발생하여 P'이 계속 감소하나 실트질에서 나타나는 상태변형선을 지나서는 체적팽창경향이 나타나며 전단강도가 증가하고 있다. 과압밀 시료는 체적변형 경향으로 순수실트와는 상당히 다른 거동을 보임을 알 수 있었다. 수정 Camflay모델 및 항복경계면 모델을 사용하여 예측한 결과 정규압밀된 경우에는 최대강도 이전까지는 실제거동을 적절히 예측할 수 있었으나 과압밀비$(2\leqOCR\leq6)$가 커질수록 그 거동에는 정량적인 차이를 보임을 알 수 있었다.

• Geomechanics and Engineering
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• 제9권4호
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• pp.415-426
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• 2015

Researches have been done to discover ways to strengthen peat soil deposits. In this model study, fibrous peat that is the most compressible types of peat has been reinforced with precast peat columns stabilized with ordinary Portland cement and polypropylene fibres. Rowe cell consolidation tests as well as plate load tests (PLTs) were conducted on various types of test samples to evaluate the strength and deformation of untreated peat and peat reinforced by various types of columns. PLTs were conducted in a specially designed and fabricated circular steel test tank. The compression index ($C_c$) and recompression index ($C_r$) of fibrous peat samples reduced considerably upon use of precast columns. Also, PLT results confirmed the results obtained from Rowe cell tests. Use of polypropylene fibres added to cement further decreased ($C_c$) and ($C_r$) and increased load bearing capacity of untreated peat. Finite element method (FEM) using Plaxis 3D was carried out to evaluate the stress distributions along various types of tested samples and also, to compare the deformations obtained from FEM analysis with the actual maximum deformations found from PLTs. FEM results indicate that most of the induced stresses are taken on the upper portion of tested samples and reach their maximum values below the loading plate. Also, a close agreement was found between actual deformation values obtained from PLTs and values resulted from FEM analysis for various types of tested samples.

• 한국환경과학회지
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• 제13권2호
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• pp.175-180
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• 2004

A prompt gamma-ray neutron activation (PGNA) system was simulated by the Monte Carlo N-Particle transport code (MCNP-4A) to estimate the level at which the scattered photon fluence rate, the absolute efficiency of the HPGe-detector, the volume of the concrete sample and the $^{35}$ /Cl(n, ${\gamma}$) reaction rate in this sample contribute to the count rate in the NaCl concentration measurement. The n- ${\gamma}$ fluence rates at the ST-2 beam tube exit of the HANARO reactor were used as input data, and the GAMMA-X type HPGe detector was modeled to tally 1.1649 MeV ${\gamma}$ -rays emitted from the $^{35}$ Cl(n, ${\gamma}$) reaction in the concrete sample. For three cylindrical concrete samples of 13.8, 46.8 and 157.1 ㎤ volumes, respectively, the relations between the NaCl weight fractions of 0.1, 1, 2 and 5 % in each of the concrete samples and the 1.1 649 MeV pulses created in the HPGe detector model were studied. As a result, it was found that the count rate at the same NaCl concentration nearly depends on the volume of the samples in a simulated condition of the same NaCl concentration samples, and that the linearities of the NaCl concentration calibration curves were reasonable in the narrow range of the NaCl weight fraction.

• Wind and Structures
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• 제17권2호
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• pp.227-244
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• 2013

Non-stationary extreme winds such as thunderstorm downbursts are responsible for many structural damages. This research presents a time domain approach for estimating along-wind load effects on tall buildings using multiple wind speed time history samples, which are simulated from evolutionary power spectra density (EPSD) functions of non-stationary wind fluctuations using the method developed by the authors' earlier research. The influence of transient wind loads on various responses including time-varying mean, root-mean-square value and peak factor is also studied. Furthermore, a simplified model is proposed to describe the non-stationary wind fluctuation as a uniformly modulated process with a modulation function following the time-varying mean. Finally, the probabilistic extreme response and peak factor are quantified based on the up-crossing theory of non-stationary process. As compared to the time domain response analysis using limited samples of wind record, usually one sample, the analysis using multiple samples presented in this study will provide more statistical information of responses. The time domain simulation also facilitates consideration of nonlinearities of structural and wind load characteristics over previous frequency domain analysis.

• Maxillofacial Plastic and Reconstructive Surgery
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• 제19권4호
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• pp.383-394
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• 1997

This study was done to examine adherence of oral bacteria to titanium dental implant and to know the effective prophylactic antibiotics using an in vivo model. Three samples each of the implant material were set in an acrylic resin flange and placed in the maxillary buccal sulcus of twenty volunteers. At 6- and 54-hour intervals, each sample was placed on blood agar plate (BAP) and chocolate agar, and then they were incubated and identified. Also antibiotic susceptibility test was performed. The results obtained mere as follows ; 1. The microorganisms were chain-like Gram positive cocci and staphyline Gram positive cocci, Gram positive bacilli in order of frequency were found at 6-hour and 54-hour samples by Gram staining. 2. Streptococci was found predominantly at both 6-hour and 54-hour samples, but number of streptococci was decreased as compared to 6-hour samples. 3. There was no difference in the bacterial species adherent to implant between 6-hour and 54-hour samples. 4. All the microbes were sensitive to AMC (amoxacillin clavulanic acid), chloramphenicol, quinolone and vancomycin in the antibiotic susceptibility test. Above results suggest that streptococcus are mainly adhered to titanium implant after implant was placed in the oral cavity and AMC is the most recommendable antibiotics to prevent the peri-implant inflammation.

• Computers and Concrete
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• 제15권1호
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• pp.89-101
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• 2015

In this paper, Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) models were discussed to determine the compressive strength of clinker mortars cured for 1, 2, 7 and 28 days. In the experimental stage, 1288 mortar samples were produced from 322 different clinker specimens and compressive strength tests were performed on these samples. Chemical properties of the clinker samples were also determined. In the modeling stage, these experimental results were used to construct the models. In the models tricalcium silicate ($C_3S$), dicalcium silicate ($C_2S$), tricalcium aluminate ($C_3A$), tetracalcium alumina ferrite ($C_4AF$), blaine values, specific gravity and age of samples were used as inputs and the compressive strength of clinker samples was used as output. The approximate reasoning ability of the models compared using some statistical parameters. As a result, ANN has shown satisfying relation with experimental results and suggests an alternative approach to evaluate compressive strength estimation of clinker mortars using related inputs. Furthermore MLR model showed a poor ability to predict.

• 한국연초학회지
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• 제22권1호
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• pp.76-83
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• 2000

This study was carried out to predict blending ratio of cut tobacco(CT), expanded stem(ES), and expanded cut tobacco(ECT) in cigarettes. CT, ES, and ECT samples from A brand were, ground and blended with reference to A blending ratio, and scanned by near infrared spectroscopy(NIRSystem Co., Model 6500). Calibration equations were developed and then determined blending ratio by NIRS. The standard error of calibration(SEC) and performance(SEP) of C factory samples between NIRS and known blending ratio were 0.97%, 1.93% for CT, 0.50%, 1.12 % for ES and 0.68%, 1.10% for ECT, respectively. The SEP of CT, ES and ECT of Band D factory samples determined by C factory calibration equation were more inaccurate than those of C factory samples determined by C factory calibration equations. These results were caused by the difference of CT, ES and ECT spectra followed by each factory. The SEP of CT, ES and ECT of Band D factories determined by calibration equations derived from each factory samples were more accurate than those of determined by calibration equation derived from C factory samples. Each factory SEP of CT, ES and ECT determined by calibration equation derived from all calibration samples(B+C+D factory) was similar to that determined by calibration equation derived from each factory samples. To improve the analytical inaccuracy caused by spectra difference, we need to apply a specific calibration equation for each factory sample. Data in development of specific calibrations between sample and NIRS spectra might supply a method for rapid determination of blending ratio of CT, ES, and ECT.

• Journal of Information Processing Systems
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• 제20권3호
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• pp.375-390
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• 2024

Edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud-edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deep-learning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud-edge collaborative computing architectures.

• Communications for Statistical Applications and Methods
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• 제19권3호
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• pp.495-500
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• 2012

We consider a Bayesian test of independence in a two-way contingency table that has some zero cells. To do this, we take a three-stage hierarchical Bayesian model under each hypothesis. For prior, we use Dirichlet density to model the marginal cell and each cell probabilities. Our method does not require complicated computation such as a Metropolis-Hastings algorithm to draw samples from each posterior density of parameters. We draw samples using a Gibbs sampler with a grid method. For complicated posterior formulas, we apply the Monte-Carlo integration and the sampling important resampling algorithm. We compare the values of the Bayes factor with the results of a chi-square test and the likelihood ratio test.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.1883-1887
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• 1991

Identification theory for linear discrete system has been presented by a great many reference, but research works for identification of continuous-time system are less than preceding identification. In fact, a great man), systems for engineering are continuous-time systems, hence, research for identification of continuous-time system has important meaning. This paper offers the following results: 1. Corresponding relations for the parameters of continuous-time model and discrete model may be shown, when single input-output system has general characteristic roots. 2. To do identification of single variable continuity linear system with stability constraints from samples of input-output data, it is necessary to use optimization with stability constraints. 3. Main results of this paper may be explained by a simple example.