• Journal of the Korean Geotechnical Society
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• v.27 no.6
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• pp.27-37
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• 2011

Recently, pressure grouting is widely being used in construction site for strength improvement of ground and water proof, reinforcement and so on. It is necessarily required to estimate an appropriate injection pressure and injection time for economical and reasonable construction in the site through the size and shape of the hardened grout measured according to ground condition. However, sampling for the hardened grout is time-consuming and needs high cost on preliminary test in the site. The system which could predict the size and shape of the hardened grout does not exist until now. Thus, numerical method based on VOF method and porous model was used for the calibration chamber injection test with injection pressure (50 kPa, 100 kPa, 150 kPa) in this study. The results indicate that the numerical technique based on VOF method and porous model among CFD analysis is expected to be a basic study for the prediction of the behavior and solidification of pressure grouting.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.9 s.339
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• pp.35-40
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• 2005

A digitally controlled phase-locked loop (DCPLL) is described. The DCPLL has basically the same structure as a conventional analog PLL except for a tracking analog-to-digital converter (ADC). The tracking ADC generates the control signal for voltage controlled oscillator. Since the DCPLL employs neither digitally controlled oscillator nor time-to-digital converter-the key building blocks of digital PLL (DPLL), there is no need for the 03de-off between jitter, power consumption and silicon area. The DCPLL was implemented in a $0.18\mu$m CMOS process and the active area is 1mm $\times$0.35 mm The DCPLL consumes S9mW during the normal opuation and $984\{mu}W$ during the power-down mode from a 1.8V supply. The DCPLL shows 16.8ps ms jitter.

• The Journal of Engineering Geology
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• v.5 no.1
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• pp.75-93
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• 1995

This study investigates the adsorption capacity of the gibbsite and the clay on the development of admixed liner. The gibbsite is produced as a by-product in the pretreatment process for cleaning and coloring of Alurninurn sash. From the study, following conclusions were obtained: 1) The adsorption of metals such as Cu(II), Cd(II), and Ni(II) and phenol on gibbsite and l:entonite was equilibrated rather quickly(12 ~48 hrs ). 2) The rate and extent of adsorption is a function of surface area the adsorbent having. 3) The Larigmuir isotherm is found to be more suitable than Freundlich isotherm for the adsorption analysis of heavy metals on gibbsite and bentonite. 4) In case of phenol, Freundlich isotherm, whose N value is close to 1, i.e., close to linear isotherm, is more fit to describe the adsorption on gibbsite and bentonite. 5) The amount of metals and phenol adsorbed is found to be in the following order : Adsorbent : $2{\mu}m-Al(OH)_3$ > Mixed Solid > $12{\mu}m-Al(OH)_3$ > Na-Bentonite > $30{\mu}m-Al(OH)_3$

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.123-123
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• 2012

일강수량은 수공구조물 설계 및 수자원계획을 수립하기 위한 입력 자료로 이용된다. 일반적으로 수자원계획은 장기적인 목적을 가지고 수행되어지며, 장기간의 일강수량 자료를 필요로 한다. 하지만 장기간의 일강수량 자료의 획득의 어려움으로 단기간의 일강수량자료를 이용하여 모의한 장기간 강수자료를 이용하게 된다. 이처럼 수자원계획의 수립에 있어서 일강수량 모의기법의 성능은 수자원계획의 신뢰성 및 결과에 큰 영향을 준다. 일강수량 모의기법은 국내외적으로 매우 활발하게 이루어지고 있으며, 수자원계획 및 수공구조물 설계 외에도 매우 다양한 목적으로 활용되어 지고 있다. 일강수량을 모의기법 중 강수계열의 단기간의 기억(memory)을 활용한 Markov Chain 모형이 가장 일반적이지만, 기존 Markov Chain 모형을 통한 일강수량 모의는 극치강수량을 재현하기 어렵다는 문제점이 있다. 또한, 일강수량 모의 기법의 목적인 수자원계획 및 수공구조물 설계 등의 입력자료로 활용되어지기 위해서는 모의 결과가 유역내 지점별 공간 상관성을 재현함으로써 모형의 우수성과 자료결과의 신뢰성을 확보할 수 있어야 하겠다. 이러한 점에서 본 연구에서는 내삽에서 우수한 재현능력을 갖는 핵 밀도함수와 극치강수량 재현에 유리한 GPD분포의 특징을 함께 고려할 수 있는 불연속 Kernel-Pareto Distribution 기반에 공간상관성 재현 알고리즘을 결합한 일강수량모의기법을 개발하였다. 한강유역의 18개 강수지점에 대해서 기존 Gamma분포를 사용한 Markov Chain 모형과 본 연구에서 제안한 방법을 적용하여 모형을 평가해 보고자 한다. Gamma 분포기반 Markov Chain 모형의 경우 일강수량 모의 시 1차모멘트인 평균과 2-3차 모멘트 모두 효과적으로 재현하지 못하는 문제점이 나타났다. 그러나 본 연구에서 적용한 다지점 불연속 Kernel-Pareto 분포 모형은 강수계열의 평균적인 특성뿐만 아니라 표준편차 및 왜곡도의 경우에도 관측치의 통계특성을 매우 효과적으로 재현하며, 100년빈도 강수량 모의결과 기존 모의모형의 문제점을 보완할 수 있는 개선된 결과를 보여주었다. 본 연구에서 제시한 방법론은 유역내의 공간상관성을 재현하며, 평균 및 중간값 등 낮은 차수의 모멘트 등 일강수량 분포특성을 더욱 효과적으로 모의할 수 장점을 확인하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.88-88
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• 2011

수자원에서 일강수량 모의기법은 다양한 목적으로 활용되고 있으며 기본적으로 수공구조물 설계 및 수자원계획을 수립하기 위한 입력 자료로서 이용된다. 수자원계획은 장기적인 목적을 가지고 수행되는 것이 일반적이며 우리가 목표로 하는 장기간의 일강수량자료의 획득이 어렵기 때문에 단기간의 일강수량자료를 장기 모의하여 이용하게 된다. 일강수량을 모의하는데 있어서 강수계열의 단기간의 기억(memory)을 활용한 Markov Chain 모형이 가장 일반적이며, 기존 Markov Chain 모형을 통한 일강수량 모의에서 발생하는 가장 큰 문제점은 극치강수량을 재현하기 어렵다는 점이다. 이러한 문제점으로 인해 수자원 계획을 수립하는데 있어서 불확실성을 가중시키고 있다. 특히 일강수량 모의기법을 통해서 추정되는 빈도강수량의 과소추정으로 인해 수공구조물 설계 시에 신뢰성을 확보하는 데 문제점이 있다. 이러한 점에서 본 연구에서는 기존 Markov Chain 모형에서 일강수량에 평균적인 특성과 극치특성을 동시에 재현할 수 있도록 불연속 Kernel-Pareto Distribution 기반에 일강수량모의기법을 개발하였다. 한강유역의 3개 강수지점에 대해서 기존 Markov Chain 모형과 본 연구에서 제안한 방법을 적용한 결과 여름의 일강수량 모의 시 1차모멘트인 평균과 2-3차 모멘트 모두 효과적으로 재현하지 못하는 문제점이 나타났다. 그러나 본 연구에서 제안한 불연속 Kernel-Pareto 분포형 기반 Markov Chain 모형은 여름의 일강수량 모의 시 강수계열의 평균적인 특성뿐만 아니라 표준편차 및 왜곡도의 경우에도 관측치의 통계특성을 매우 효과적으로 재현하는 것으로 나타났다. 본 연구에서 제시한 방법론은 전체적으로 기존 Markov Chain 모형에 비해 극치강수량을 재현하는데 유리한 기법으로 판단되며, 또한 극치강수량을 일반강수량으로부터 분리하여 모의함으로서 평균 및 중간값 등 낮은 차수에 모멘트 등 일강수량에 전체적인 분포특성을 더욱 효과적으로 모의할 수 장점을 확인하였다.

• Journal of the Korean GEO-environmental Society
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• v.12 no.4
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• pp.17-24
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• 2011

For the purpose of cut off and ground stabilization, water glass chemical grouting method using sodium silicate has problems of weakening durability and ground water pollution because leaching was conducted when the homogel is exposed to the ground water as time elapses. The purpose of this study is to identify the effect of alkali silica sol ground injection materials, it was compared with the sodium silicate ground injection materials using water glasses. For sodium silicate and alkali silica sol by mixing each case is divided into four different specimens were made and tested. The characteristic of alkali silica sol ground injection material was analyzed by unconfined compression test and environmental impact statement of ordinary portland cement and blast furnace slag cement. Alkali silica sol specimens were made mixing A-solution and B-solution in the proportion of one on one. Through this study, alkali silica sol ground injection mixing blast furnace slag cement has excellent strength and environment-friendly.

• Journal of the Korean Recycled Construction Resources Institute
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• v.11 no.4
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• pp.324-331
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• 2023

In this study, the hydration and carbonation properties of circulating fluidized bed boiler (CFBC) ash with different free-CaO contents were investigated. In addition, the possibility of utilizing CFBC ash with a high free-CaO content as a cementitious material was investigated by carbonation curing as a pretreatment. The CFBC ash with high free-CaO content exhibited rapid setting behavior and low early compressive strength when mixed with cement. For CFBC ash with high free-CaO content, carbon dioxide capture increased with the duration of carbonization curing. In addition, the free-CaO value decreased together, indicating that the free-CaO reacted with carbon dioxide. When the CFBC ash with high free-CaO content was pretreated by carbonation, no fresh set appeared, and the initial compressive strength was improved. From the results of this study, it is confirmed that CFBC ash with high free-CaO content has a high potential to be utilized as a cementitious material through proper carbonation curing.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.2
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• pp.129-152
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• 2024

In the application of deep learning object detection via CCTV in tunnels, a large number of false positive detections occur due to the poor environmental conditions of tunnels, such as low illumination and severe perspective effect. This problem directly impacts the reliability of the tunnel CCTV-based accident detection system reliant on object detection performance. Hence, it is necessary to reduce the number of false positive detections while also enhancing the number of true positive detections. Based on a deep learning object detection model, this paper proposes a false positive data training method that not only reduces false positives but also improves true positive detection performance through retraining of false positive data. This paper's false positive data training method is based on the following steps: initial training of a training dataset - inference of a validation dataset - correction of false positive data and dataset composition - addition to the training dataset and retraining. In this paper, experiments were conducted to verify the performance of this method. First, the optimal hyperparameters of the deep learning object detection model to be applied in this experiment were determined through previous experiments. Then, in this experiment, training image format was determined, and experiments were conducted sequentially to check the long-term performance improvement through retraining of repeated false detection datasets. As a result, in the first experiment, it was found that the inclusion of the background in the inferred image was more advantageous for object detection performance than the removal of the background excluding the object. In the second experiment, it was found that retraining by accumulating false positives from each level of retraining was more advantageous than retraining independently for each level of retraining in terms of continuous improvement of object detection performance. After retraining the false positive data with the method determined in the two experiments, the car object class showed excellent inference performance with an AP value of 0.95 or higher after the first retraining, and by the fifth retraining, the inference performance was improved by about 1.06 times compared to the initial inference. And the person object class continued to improve its inference performance as retraining progressed, and by the 18th retraining, it showed that it could self-improve its inference performance by more than 2.3 times compared to the initial inference.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.2 no.4
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• pp.1-9
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• 1982

Variation of soil properties is studied by permeating injection of chemical grouts, such as cement type, water-glass type and acrylamide type, to the same soil samples with different densities. Moreover, injection tests using specially prepared equipments of 1.0 shot system and 1. 5 shot system are attempted to investigate permeating injection effects in highly compacted soil and in the presence of ground water. The main factor which causes the improvement of cut-off effect and shearing strength is the cohesion of soil. The strength in the loose state is fundamentally governed by the membrane cohesion, meanwhile, in the loose state is governed by the structural cohesion. Injection effects under the ground water flow is considerably decreased, and effective gelling ratio of approximate 45~80% is observed by variation of velocity and gel time, besides grading of injection materials has high relation with permeation and traveling length but has little relation with effective gelling ratio. Permeating injection effects, such as gelling scope, gelling strength in highly compaoted soil conditions can be confirmed by penetration resistance diagram and iso-strength curve.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.3B
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• pp.277-284
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• 2011

The limitations of existing Markov chain model for reproducing extreme rainfalls are a known problem, and the problems have increased the uncertainties in establishing water resources plans. Especially, it is very difficult to secure reliability of water resources structures because the design rainfall through the existing Markov chain model are significantly underestimated. In this regard, aims of this study were to develop a new daily rainfall simulation model which is able to reproduce both mean and high order moments such as variance and skewness using a piecewise Kernel-Pareto distribution. The proposed methods were applied to summer and fall season rainfall at three stations in Han river watershed in Korea. The proposed Kernel-Pareto distribution based Markov chain model has been shown to perform well at reproducing most of statistics such as mean, standard deviation and skewness while the existing Gamma distribution based Markov chain model generally fails to reproduce high order moments. It was also confirmed that the proposed model can more effectively reproduce low order moments such as mean and median as well as underlying distribution of daily rainfall series by modeling extreme rainfall separately.